Data Analysis: Evaluation Metrics for Supervised Learning Models of Machine L...Md. Main Uddin Rony
This document discusses various machine learning evaluation metrics for supervised learning models. It covers classification, regression, and ranking metrics. For classification, it describes accuracy, confusion matrix, log-loss, and AUC. For regression, it discusses RMSE and quantiles of errors. For ranking, it explains precision-recall, precision-recall curves, F1 score, and NDCG. The document provides examples and visualizations to illustrate how these metrics are calculated and used to evaluate model performance.
Slide explaining the distinction between bagging and boosting while understanding the bias variance trade-off. Followed by some lesser known scope of supervised learning. understanding the effect of tree split metric in deciding feature importance. Then understanding the effect of threshold on classification accuracy. Additionally, how to adjust model threshold for classification in supervised learning.
Note: Limitation of Accuracy metric (baseline accuracy), alternative metrics, their use case and their advantage and limitations were briefly discussed.
Supervised learning: discover patterns in the data that relate data attributes with a target (class) attribute.
These patterns are then utilized to predict the values of the target attribute in future data instances.
Unsupervised learning: The data have no target attribute.
We want to explore the data to find some intrinsic structures in them.
This presentation introduces clustering analysis and the k-means clustering technique. It defines clustering as an unsupervised method to segment data into groups with similar traits. The presentation outlines different clustering types (hard vs soft), techniques (partitioning, hierarchical, etc.), and describes the k-means algorithm in detail through multiple steps. It discusses requirements for clustering, provides examples of applications, and reviews advantages and disadvantages of k-means clustering.
This presentation introduces naive Bayesian classification. It begins with an overview of Bayes' theorem and defines a naive Bayes classifier as one that assumes conditional independence between predictor variables given the class. The document provides examples of text classification using naive Bayes and discusses its advantages of simplicity and accuracy, as well as its limitation of assuming independence. It concludes that naive Bayes is a commonly used and effective classification technique.
Unsupervised Machine Learning Ml And How It WorksSlideTeam
Unsupervised Machine Learning ML and how it works is for the mid level managers to give information about what is unsupervised machine learning, types of unsupervised learning, and its disadvantages. You can also know how unsupervised machine learning works to understand supervised machine learning in a better way for business growth. https://bit.ly/3fTQ7iI
Data Analysis: Evaluation Metrics for Supervised Learning Models of Machine L...Md. Main Uddin Rony
This document discusses various machine learning evaluation metrics for supervised learning models. It covers classification, regression, and ranking metrics. For classification, it describes accuracy, confusion matrix, log-loss, and AUC. For regression, it discusses RMSE and quantiles of errors. For ranking, it explains precision-recall, precision-recall curves, F1 score, and NDCG. The document provides examples and visualizations to illustrate how these metrics are calculated and used to evaluate model performance.
Slide explaining the distinction between bagging and boosting while understanding the bias variance trade-off. Followed by some lesser known scope of supervised learning. understanding the effect of tree split metric in deciding feature importance. Then understanding the effect of threshold on classification accuracy. Additionally, how to adjust model threshold for classification in supervised learning.
Note: Limitation of Accuracy metric (baseline accuracy), alternative metrics, their use case and their advantage and limitations were briefly discussed.
Supervised learning: discover patterns in the data that relate data attributes with a target (class) attribute.
These patterns are then utilized to predict the values of the target attribute in future data instances.
Unsupervised learning: The data have no target attribute.
We want to explore the data to find some intrinsic structures in them.
This presentation introduces clustering analysis and the k-means clustering technique. It defines clustering as an unsupervised method to segment data into groups with similar traits. The presentation outlines different clustering types (hard vs soft), techniques (partitioning, hierarchical, etc.), and describes the k-means algorithm in detail through multiple steps. It discusses requirements for clustering, provides examples of applications, and reviews advantages and disadvantages of k-means clustering.
This presentation introduces naive Bayesian classification. It begins with an overview of Bayes' theorem and defines a naive Bayes classifier as one that assumes conditional independence between predictor variables given the class. The document provides examples of text classification using naive Bayes and discusses its advantages of simplicity and accuracy, as well as its limitation of assuming independence. It concludes that naive Bayes is a commonly used and effective classification technique.
Unsupervised Machine Learning Ml And How It WorksSlideTeam
Unsupervised Machine Learning ML and how it works is for the mid level managers to give information about what is unsupervised machine learning, types of unsupervised learning, and its disadvantages. You can also know how unsupervised machine learning works to understand supervised machine learning in a better way for business growth. https://bit.ly/3fTQ7iI
Abstract: This PDSG workshop introduces basic concepts of splitting a dataset for training a model in machine learning. Concepts covered are training, test and validation data, serial and random splitting, data imbalance and k-fold cross validation.
Level: Fundamental
Requirements: No prior programming or statistics knowledge required.
Decision trees are a type of supervised learning algorithm used for classification and regression. ID3 and C4.5 are algorithms that generate decision trees by choosing the attribute with the highest information gain at each step. Random forest is an ensemble method that creates multiple decision trees and aggregates their results, improving accuracy. It introduces randomness when building trees to decrease variance.
The document discusses random forest, an ensemble classifier that uses multiple decision tree models. It describes how random forest works by growing trees using randomly selected subsets of features and samples, then combining the results. The key advantages are better accuracy compared to a single decision tree, and no need for parameter tuning. Random forest can be used for classification and regression tasks.
Classification techniques in data miningKamal Acharya
The document discusses classification algorithms in machine learning. It provides an overview of various classification algorithms including decision tree classifiers, rule-based classifiers, nearest neighbor classifiers, Bayesian classifiers, and artificial neural network classifiers. It then describes the supervised learning process for classification, which involves using a training set to construct a classification model and then applying the model to a test set to classify new data. Finally, it provides a detailed example of how a decision tree classifier is constructed from a training dataset and how it can be used to classify data in the test set.
This document discusses various machine learning techniques for classification and prediction. It covers decision tree induction, tree pruning, Bayesian classification, Bayesian belief networks, backpropagation, association rule mining, and ensemble methods like bagging and boosting. Classification involves predicting categorical labels while prediction predicts continuous values. Key steps for preparing data include cleaning, transformation, and comparing different methods based on accuracy, speed, robustness, scalability, and interpretability.
The document discusses the K-nearest neighbors (KNN) algorithm, a simple machine learning algorithm used for classification problems. KNN works by finding the K training examples that are closest in distance to a new data point, and assigning the most common class among those K examples as the prediction for the new data point. The document covers how KNN calculates distances between data points, how to choose the K value, techniques for handling different data types, and the strengths and weaknesses of the KNN algorithm.
1. Machine learning is a set of techniques that use data to build models that can make predictions without being explicitly programmed.
2. There are two main types of machine learning: supervised learning, where the model is trained on labeled examples, and unsupervised learning, where the model finds patterns in unlabeled data.
3. Common machine learning algorithms include linear regression, logistic regression, decision trees, support vector machines, naive Bayes, k-nearest neighbors, k-means clustering, and random forests. These can be used for regression, classification, clustering, and dimensionality reduction.
The document discusses modelling and evaluation in machine learning. It defines what models are and how they are selected and trained for predictive and descriptive tasks. Specifically, it covers:
1) Models represent raw data in meaningful patterns and are selected based on the problem and data type, like regression for continuous numeric prediction.
2) Models are trained by assigning parameters to optimize an objective function and evaluate quality. Cross-validation is used to evaluate models.
3) Predictive models predict target values like classification to categorize data or regression for continuous targets. Descriptive models find patterns without targets for tasks like clustering.
4) Model performance can be affected by underfitting if too simple or overfitting if too complex,
Decision tree is a type of supervised learning algorithm (having a pre-defined target variable) that is mostly used in classification problems. It is a tree in which each branch node represents a choice between a number of alternatives, and each leaf node represents a decision.
You will learn the basic concepts of machine learning classification and will be introduced to some different algorithms that can be used. This is from a very high level and will not be getting into the nitty-gritty details.
Machine Learning in Healthcare DiagnosticsLarry Smarr
Machine learning and artificial intelligence are rapidly transforming healthcare and medicine. Advances in genetic sequencing have enabled the mapping of human and microbial genomes at low costs. Researchers are using machine learning to analyze genomic and microbiome data to better understand health and disease. Non-von Neumann brain-inspired computing architectures are being developed for machine learning applications and could accelerate medical research and diagnostics. These technologies may help create personalized health coaching and move medicine from reactive sickcare to proactive healthcare.
Machine Learning Performance metrics for classificationKuppusamy P
The document discusses different performance metrics for evaluating outlier detection models: recall, false alarm rate, ROC curves, and AUC. ROC curves plot the true positive rate against the false positive rate. AUC measures the entire area under the ROC curve, indicating how well a model can distinguish between classes. F1 score provides a balanced measure of a model's precision and recall that is better than accuracy alone when classes are unevenly distributed. Accuracy can be high even when a model misses many true outliers, so F1 score is a more appropriate metric for outlier detection evaluation.
This document provides an overview of bag-of-words models for image classification. It discusses how bag-of-words models originated from texture recognition and document classification. Images are represented as histograms of visual word frequencies. A visual vocabulary is learned by clustering local image features, and each cluster center becomes a visual word. Both discriminative methods like support vector machines and generative methods like Naive Bayes are used to classify images based on their bag-of-words representations.
Feature Engineering in Machine LearningKnoldus Inc.
In this Knolx we are going to explore Data Preprocessing and Feature Engineering Techniques. We will also understand what is Feature Engineering and its importance in Machine Learning. How Feature Engineering can help in getting the best results from the algorithms.
K-Nearest neighbor is one of the most commonly used classifier based in lazy learning. It is one of the most commonly used methods in recommendation systems and document similarity measures. It mainly uses Euclidean distance to find the similarity measures between two data points.
This document provides an overview of machine learning concepts including supervised learning, unsupervised learning, and reinforcement learning. It explains that supervised learning involves learning from labeled examples, unsupervised learning involves categorizing without labels, and reinforcement learning involves learning behaviors to achieve goals through interaction. The document also discusses regression vs classification problems, the learning and testing process, and examples of machine learning applications like customer profiling, face recognition, and handwritten character recognition.
This document discusses machine learning concepts like supervised and unsupervised learning. It explains that supervised learning uses known inputs and outputs to learn rules while unsupervised learning deals with unknown inputs and outputs. Classification and regression are described as types of supervised learning problems. Classification involves categorizing data into classes while regression predicts continuous, real-valued outputs. Examples of classification and regression problems are provided. Classification models like heuristic, separation, regression and probabilistic models are also mentioned. The document encourages learning more about classification algorithms in upcoming videos.
This document discusses various performance metrics used to evaluate machine learning models, with a focus on classification metrics. It defines key metrics like accuracy, precision, recall, and specificity using a cancer detection example. Accuracy is only useful when classes are balanced, while precision captures true positives and recall focuses on minimizing false negatives. The document emphasizes that the appropriate metric depends on the problem and whether minimizing false positives or false negatives is more important. Confusion matrices are also introduced as a way to visualize model performance.
Performance metrics are used to evaluate machine learning algorithms and models. Key methods include confusion matrix, accuracy, precision, recall, specificity, and F1 score. The confusion matrix is a table that allows visualization of model performance, while accuracy measures correct predictions over total predictions. Precision focuses on avoiding false positives and recall focuses on avoiding false negatives. The F1 score calculates the harmonic mean of precision and recall to provide a single combined metric. These metrics help select the best performing algorithm and optimize model performance.
Abstract: This PDSG workshop introduces basic concepts of splitting a dataset for training a model in machine learning. Concepts covered are training, test and validation data, serial and random splitting, data imbalance and k-fold cross validation.
Level: Fundamental
Requirements: No prior programming or statistics knowledge required.
Decision trees are a type of supervised learning algorithm used for classification and regression. ID3 and C4.5 are algorithms that generate decision trees by choosing the attribute with the highest information gain at each step. Random forest is an ensemble method that creates multiple decision trees and aggregates their results, improving accuracy. It introduces randomness when building trees to decrease variance.
The document discusses random forest, an ensemble classifier that uses multiple decision tree models. It describes how random forest works by growing trees using randomly selected subsets of features and samples, then combining the results. The key advantages are better accuracy compared to a single decision tree, and no need for parameter tuning. Random forest can be used for classification and regression tasks.
Classification techniques in data miningKamal Acharya
The document discusses classification algorithms in machine learning. It provides an overview of various classification algorithms including decision tree classifiers, rule-based classifiers, nearest neighbor classifiers, Bayesian classifiers, and artificial neural network classifiers. It then describes the supervised learning process for classification, which involves using a training set to construct a classification model and then applying the model to a test set to classify new data. Finally, it provides a detailed example of how a decision tree classifier is constructed from a training dataset and how it can be used to classify data in the test set.
This document discusses various machine learning techniques for classification and prediction. It covers decision tree induction, tree pruning, Bayesian classification, Bayesian belief networks, backpropagation, association rule mining, and ensemble methods like bagging and boosting. Classification involves predicting categorical labels while prediction predicts continuous values. Key steps for preparing data include cleaning, transformation, and comparing different methods based on accuracy, speed, robustness, scalability, and interpretability.
The document discusses the K-nearest neighbors (KNN) algorithm, a simple machine learning algorithm used for classification problems. KNN works by finding the K training examples that are closest in distance to a new data point, and assigning the most common class among those K examples as the prediction for the new data point. The document covers how KNN calculates distances between data points, how to choose the K value, techniques for handling different data types, and the strengths and weaknesses of the KNN algorithm.
1. Machine learning is a set of techniques that use data to build models that can make predictions without being explicitly programmed.
2. There are two main types of machine learning: supervised learning, where the model is trained on labeled examples, and unsupervised learning, where the model finds patterns in unlabeled data.
3. Common machine learning algorithms include linear regression, logistic regression, decision trees, support vector machines, naive Bayes, k-nearest neighbors, k-means clustering, and random forests. These can be used for regression, classification, clustering, and dimensionality reduction.
The document discusses modelling and evaluation in machine learning. It defines what models are and how they are selected and trained for predictive and descriptive tasks. Specifically, it covers:
1) Models represent raw data in meaningful patterns and are selected based on the problem and data type, like regression for continuous numeric prediction.
2) Models are trained by assigning parameters to optimize an objective function and evaluate quality. Cross-validation is used to evaluate models.
3) Predictive models predict target values like classification to categorize data or regression for continuous targets. Descriptive models find patterns without targets for tasks like clustering.
4) Model performance can be affected by underfitting if too simple or overfitting if too complex,
Decision tree is a type of supervised learning algorithm (having a pre-defined target variable) that is mostly used in classification problems. It is a tree in which each branch node represents a choice between a number of alternatives, and each leaf node represents a decision.
You will learn the basic concepts of machine learning classification and will be introduced to some different algorithms that can be used. This is from a very high level and will not be getting into the nitty-gritty details.
Machine Learning in Healthcare DiagnosticsLarry Smarr
Machine learning and artificial intelligence are rapidly transforming healthcare and medicine. Advances in genetic sequencing have enabled the mapping of human and microbial genomes at low costs. Researchers are using machine learning to analyze genomic and microbiome data to better understand health and disease. Non-von Neumann brain-inspired computing architectures are being developed for machine learning applications and could accelerate medical research and diagnostics. These technologies may help create personalized health coaching and move medicine from reactive sickcare to proactive healthcare.
Machine Learning Performance metrics for classificationKuppusamy P
The document discusses different performance metrics for evaluating outlier detection models: recall, false alarm rate, ROC curves, and AUC. ROC curves plot the true positive rate against the false positive rate. AUC measures the entire area under the ROC curve, indicating how well a model can distinguish between classes. F1 score provides a balanced measure of a model's precision and recall that is better than accuracy alone when classes are unevenly distributed. Accuracy can be high even when a model misses many true outliers, so F1 score is a more appropriate metric for outlier detection evaluation.
This document provides an overview of bag-of-words models for image classification. It discusses how bag-of-words models originated from texture recognition and document classification. Images are represented as histograms of visual word frequencies. A visual vocabulary is learned by clustering local image features, and each cluster center becomes a visual word. Both discriminative methods like support vector machines and generative methods like Naive Bayes are used to classify images based on their bag-of-words representations.
Feature Engineering in Machine LearningKnoldus Inc.
In this Knolx we are going to explore Data Preprocessing and Feature Engineering Techniques. We will also understand what is Feature Engineering and its importance in Machine Learning. How Feature Engineering can help in getting the best results from the algorithms.
K-Nearest neighbor is one of the most commonly used classifier based in lazy learning. It is one of the most commonly used methods in recommendation systems and document similarity measures. It mainly uses Euclidean distance to find the similarity measures between two data points.
This document provides an overview of machine learning concepts including supervised learning, unsupervised learning, and reinforcement learning. It explains that supervised learning involves learning from labeled examples, unsupervised learning involves categorizing without labels, and reinforcement learning involves learning behaviors to achieve goals through interaction. The document also discusses regression vs classification problems, the learning and testing process, and examples of machine learning applications like customer profiling, face recognition, and handwritten character recognition.
This document discusses machine learning concepts like supervised and unsupervised learning. It explains that supervised learning uses known inputs and outputs to learn rules while unsupervised learning deals with unknown inputs and outputs. Classification and regression are described as types of supervised learning problems. Classification involves categorizing data into classes while regression predicts continuous, real-valued outputs. Examples of classification and regression problems are provided. Classification models like heuristic, separation, regression and probabilistic models are also mentioned. The document encourages learning more about classification algorithms in upcoming videos.
This document discusses various performance metrics used to evaluate machine learning models, with a focus on classification metrics. It defines key metrics like accuracy, precision, recall, and specificity using a cancer detection example. Accuracy is only useful when classes are balanced, while precision captures true positives and recall focuses on minimizing false negatives. The document emphasizes that the appropriate metric depends on the problem and whether minimizing false positives or false negatives is more important. Confusion matrices are also introduced as a way to visualize model performance.
Performance metrics are used to evaluate machine learning algorithms and models. Key methods include confusion matrix, accuracy, precision, recall, specificity, and F1 score. The confusion matrix is a table that allows visualization of model performance, while accuracy measures correct predictions over total predictions. Precision focuses on avoiding false positives and recall focuses on avoiding false negatives. The F1 score calculates the harmonic mean of precision and recall to provide a single combined metric. These metrics help select the best performing algorithm and optimize model performance.
Threshold setting for reduction of false positivesReshma Sekar
This document discusses the importance of having a low false positive rate when building classification models, especially for applications like cancer prediction. It explains that sensitivity measures the proportion of true positives predicted, while specificity measures the proportion of true negatives predicted. An example is given of a model that predicts cancer, with a sensitivity of 0.75 and specificity of 0.99. The document also introduces ROC curves, which plot the sensitivity vs. false positive rate for a model. The goal is to find the threshold on the curve that gives a high sensitivity but low false positive rate. Examples are given of ROC scores for breast cancer and fraud detection models.
This document provides an overview of key concepts in sampling, including population, sample, sampling frame, probability sampling, and non-probability sampling. It discusses the qualities of a probability sample, including how findings from a random sample can be generalized to the population. It also covers sample size considerations and different types of error in sampling, such as sampling error and non-sampling error.
Confusion matrix and classification evaluation metricsMinesh A. Jethva
This document discusses classification evaluation metrics and their limitations. It introduces the confusion matrix and metrics calculated from it such as precision, recall, F1-score, and accuracy. The summary highlights that these metrics can be "hacked" and misleading. More robust alternatives like balanced accuracy and MCC are presented that account for true negatives and are not as affected by class imbalance. Comprehensive reporting of multiple metrics from different perspectives is recommended for fully understanding a model's performance.
Module 4: Model Selection and EvaluationSara Hooker
Delta Analytics is a 501(c)3 non-profit in the Bay Area. We believe that data is powerful, and that anybody should be able to harness it for change. Our teaching fellows partner with schools and organizations worldwide to work with students excited about the power of data to do good.
Welcome to the course! These modules will teach you the fundamental building blocks and the theory necessary to be a responsible machine learning practitioner in your own community. Each module focuses on accessible examples designed to teach you about good practices and the powerful (yet surprisingly simple) algorithms we use to model data.
To learn more about our mission or provide feedback, take a look at www.deltanalytics.org.
Discriminant analysis ravi nakulan slideshareRavi Nakulan
1. The document describes analyzing a heart failure dataset using logistic regression and linear discriminant analysis (LDA) models to predict patient survival.
2. Both optimized models produced similar results, with the optimized logistic regression performing slightly better with accuracies of 83% versus 80% for LDA. However, the recall for predicting deceased patients was only 74% for both models.
3. It is recommended to obtain additional medical features and more data samples, particularly for deceased patients, which may help improve the models' ability to correctly predict outcomes and better assist treatment decisions.
This document discusses various evaluation measures used in machine learning, including accuracy, precision, recall, F1 score, and AUROC for classification problems. For regression problems, the output is continuous and no additional treatment is needed. Classification accuracy is defined as the number of correct predictions divided by the total predictions. The confusion matrix is used to calculate true positives, false positives, etc. Precision measures correct positive predictions, while recall measures all positive predictions. The F1 score balances precision and recall for imbalanced data. AUROC plots the true positive rate against the false positive rate.
Statistics is used to interpret data and draw conclusions about populations based on sample data. Hypothesis testing involves evaluating two statements (the null and alternative hypotheses) about a population using sample data. A hypothesis test determines which statement is best supported.
The key steps in hypothesis testing are to formulate the hypotheses, select an appropriate statistical test, choose a significance level, collect and analyze sample data to calculate a test statistic, determine the probability or critical value associated with the test statistic, and make a decision to reject or fail to reject the null hypothesis based on comparing the probability or test statistic to the significance level and critical value.
An example tests whether the proportion of internet users who shop online is greater than 40% using
A short introduction to sample size estimation for Research methodology workshop at Dr. BVP RMC, Pravara Institute of Medical Sciences(DU), Loni by Dr. Mandar Baviskar
This document discusses key concepts related to determining sample size for surveys:
- Confidence interval and confidence level describe the level of certainty or precision in a sample - a 95% confidence level means the true population value would fall within the confidence interval 95% of the time.
- Sample size, population size, and response distribution (how answers are split) all impact the required sample size to achieve a given confidence level and interval. Higher confidence or lower intervals require larger samples.
- For a population of 20,000, with a 50-50 response split, and 95% confidence level, the required sample size is 377 people.
Important Classification and Regression Metrics.pptxChode Amarnath
This document provides an overview of important classification and regression metrics used in machine learning. It defines metrics such as mean squared error, root mean squared error, R-squared, accuracy, precision, recall, F1 score, and AUC for evaluating regression and classification models. For each metric, it provides an intuitive explanation of what the metric measures, includes examples to illustrate how it is calculated, and discusses advantages and disadvantages as well as when the metric would be appropriate. It also explains concepts like confusion matrices, true positives/negatives, and false positives/negatives that are important for understanding various classification evaluation metrics.
1. Sample size calculation is an important part of ethical scientific research to avoid underpowered studies.
2. There are different approaches to sample size calculation depending on the study design and endpoints, such as comparing proportions, estimating confidence intervals, or analyzing time to event outcomes.
3. Key steps include defining the research hypothesis, primary and secondary endpoints, how and in whom the endpoints will be measured, and determining what difference is clinically meaningful to detect between study groups.
This document discusses various machine learning model validation techniques and ensemble methods such as bagging and boosting. It defines key concepts like overfitting, underfitting, bias-variance tradeoff, and different validation metrics. Cross validation techniques like k-fold and bootstrap are explained as ways to estimate model performance on unseen data. Bagging creates multiple models on resampled data and averages their predictions to reduce variance. Boosting iteratively adjusts weights of misclassified observations to build strong models, but risks overfitting. Gradient boosting and XGBoost are powerful ensemble methods.
Similar to Performance Metrics for Machine Learning Algorithms (20)
This document provides an overview of unsupervised learning techniques, specifically clustering algorithms. It discusses the differences between supervised and unsupervised learning, the goal of clustering to group similar observations, and provides examples of K-Means and hierarchical clustering. For K-Means clustering, it outlines the basic steps of randomly assigning clusters, calculating centroids, and repeatedly reassigning points until clusters stabilize. It also discusses selecting the optimal number of clusters K and presents pros and cons of clustering techniques.
The k-nearest neighbors (kNN) algorithm assumes similar data points exist in close proximity. It calculates the distance between data points to determine the k nearest neighbors, where k is a user-defined value. To classify a new data point, kNN finds its k nearest neighbors and assigns the most common label from those neighbors. Choosing the right k value involves testing different k and selecting the one that minimizes errors while maintaining predictive accuracy on unseen data to avoid underfitting or overfitting. While simple to implement, kNN performance degrades with large datasets due to increased computational requirements.
Machine Learning Algorithm - Naive Bayes for ClassificationKush Kulshrestha
The document discusses Naive Bayes classification. It begins by explaining Bayes' theorem and how prior probabilities can impact classification. It then provides a candy selection example to introduce Naive Bayes, noting how assuming independence between variables (even if they are dependent) simplifies calculations. The document explains how Naive Bayes works using weather data, shows an example calculation, and lists pros and cons of the technique, such as its simplicity but limitation of assuming independence.
Logistic regression is a classification algorithm used to predict binary outcomes. It transforms predictor variable values using the sigmoid function to produce a probability value between 0 and 1. The log odds of the outcome are modeled as a linear combination of the predictor variables. Positive coefficient values increase the probability of the outcome while negative values decrease the probability. Logistic regression outputs probabilities that can be converted into binary class predictions.
Assumptions of Linear Regression - Machine LearningKush Kulshrestha
There are 5 key assumptions in linear regression analysis:
1. There must be a linear relationship between the dependent and independent variables.
2. The error terms cannot be correlated with each other.
3. The independent variables cannot be highly correlated with each other.
4. The error terms must have constant variance (homoscedasticity).
5. The error terms must be normally distributed. Violations of these assumptions can result in poor model fit or inaccurate predictions. Various tests can be used to check for violations.
The document discusses how to interpret the results of linear regression analysis. It explains that p-values indicate whether predictors significantly contribute to the model, with lower p-values meaning a predictor is meaningful. Coefficients represent the change in the response variable per unit change in a predictor. The constant/intercept is meaningless if all predictors cannot realistically be zero. Goodness-of-fit is assessed using residual plots and R-squared, though high R-squared does not guarantee a good fit. Interpretation focuses on statistically significant predictors and coefficients rather than fit metrics alone.
This document provides an overview of linear regression machine learning techniques. It introduces linear regression models using one feature and multiple features. It discusses estimating regression coefficients to minimize error and find the best fitting line. The document also covers correlation, explaining that a correlation does not necessarily indicate causation. Multiple linear regression is described as fitting a linear function to multiple predictor variables. The risks of overfitting with too complex a model are noted. Code examples of implementing linear regression in Scikit-Learn and Statsmodels are referenced.
The document provides an overview of machine learning concepts including supervised and unsupervised learning algorithms. It discusses splitting data into training and test sets, training algorithms on the training set, testing algorithms on the test set, and measuring performance. For supervised learning, it describes classification and regression tasks, the bias-variance tradeoff, and how supervised algorithms learn by minimizing a loss function. For unsupervised learning, it discusses clustering, representation learning, dimensionality reduction, and exploratory analysis use cases.
Contains different types of Data Visualizations, best practices to follow for each case and what type of visualization should be made for different kinds of datasets.
This document provides an overview of descriptive statistics. It discusses key topics including measures of central tendency (mean, median, mode), measures of variability (range, IQR, variance, standard deviation, skewness, kurtosis), probability and probability distributions (binomial distribution), and how descriptive statistics is used to understand and describe data. Descriptive statistics involves numerically summarizing and presenting data through methods such as graphs, tables, and calculations without inferring conclusions about a population.
Scaling transforms data values to fall within a specific range, such as 0 to 1, without changing the data distribution. Normalization changes the data distribution to be normal. Common normalization techniques include standardization, which transforms data to have mean 0 and standard deviation 1, and Box-Cox transformation, which finds the best lambda value to make data more normal. Normalization is useful for algorithms that assume normal data distributions and can improve model performance and interpretation.
Wireless charging using electromagnetic induction, and resonance magnetic coupling. Effects and limitations, cheallenges faced and meathods to overcome. Success Case study. References included.
Time management is the act of consciously controlling how time is spent on activities to increase productivity, effectiveness, and efficiency. It involves using skills and tools to help accomplish tasks and work towards goals and deadlines. The document provides tips for effective time management, including preparing a schedule with priorities, balancing efforts, focusing on most productive times, taking breaks, tracking progress, reassessing tasks, and getting proper sleep.
How We Added Replication to QuestDB - JonTheBeachjavier ramirez
Building a database that can beat industry benchmarks is hard work, and we had to use every trick in the book to keep as close to the hardware as possible. In doing so, we initially decided QuestDB would scale only vertically, on a single instance.
A few years later, data replication —for horizontally scaling reads and for high availability— became one of the most demanded features, especially for enterprise and cloud environments. So, we rolled up our sleeves and made it happen.
Today, QuestDB supports an unbounded number of geographically distributed read-replicas without slowing down reads on the primary node, which can ingest data at over 4 million rows per second.
In this talk, I will tell you about the technical decisions we made, and their trade offs. You'll learn how we had to revamp the whole ingestion layer, and how we actually made the primary faster than before when we added multi-threaded Write Ahead Logs to deal with data replication. I'll also discuss how we are leveraging object storage as a central part of the process. And of course, I'll show you a live demo of high-performance multi-region replication in action.
The Night Patrol Car Robot is an advanced robotic system engineered for night...yeshwanth27naidu
night patrol car robot
The Night Patrol Car Robot is an advanced robotic system engineered for night time surveillance and security tasks. This robot is equipped with cutting-edge night-vision cameras, infrared sensors, and efficient communication modules, enabling it to operate effectively in low-light environments.
The future of policing may well be shaped by these technological advancements. Sensor Improvement Enhance the capabilities of sensors for better night vision, thermal imaging, and environmental awareness. Improve algorithms for real-time decision-making, threat assessment, and predictive policing
❻❸❼⓿❽❻❷⓿⓿❼ SATTA MATKA DPBOSS KALYAN MATKA RESULTS KALYAN CHART KALYAN MATKA MATKA RESULT KALYAN MATKA TIPS SATTA MATKA MATKA COM MATKA PANA JODI TODAY
❻❸❼⓿❽❻❷⓿⓿❼ SATTA MATKA DPBOSS KALYAN MATKA RESULTS KALYAN CHART KALYAN MATKA MATKA RESULT KALYAN MATKA TIPS SATTA MATKA MATKA COM MATKA PANA JODI TODAY
3. Performance Metrics
After doing the usual Feature Engineering, Selection, and of course, implementing a model and getting some output
in forms of a probability or a class, the next step is to find out how effective is the model based on some metric using
test datasets.
The metrics that you choose to evaluate your machine learning model is very important. Choice of metrics influences
how the performance of machine learning algorithms is measured and compared.
Different metrics used:
• Confusion Matrix
• Accuracy
• Precision
• Recall or Sensitivity
• Specificity
• F1 Score
• Log Loss
• Area under the curve (AUC)
• MAE – Mean Absolute Error
• MSE – Mean Squared Error
4. Confusion Matrix
Just opposite to what the name suggests, confusion matrix is one of the most intuitive and easiest metrics used for
finding the correctness and accuracy of the model. It is used for Classification problem where the output can be of
two or more types of classes.
(To Kush – Discuss regression, classification and clustering projects a bit)
Let’s say we are solving a classification problem where we are predicting whether a person is having cancer or not.
Let’s give a label of to our target variable:
1: When a person is having cancer 0: When a person is NOT having cancer.
Alright! Now that we have identified the
problem, the confusion matrix, is a table with
two dimensions (“Actual” and “Predicted”), and
sets of “classes” in both dimensions. Our Actual
classifications are columns and Predicted ones
are Rows.
Fig. 1: Confusion Matrix
5. Confusion Matrix
The Confusion matrix in itself is not a performance measure as such, a lot of the performance metrics are based on
Confusion Matrix and the numbers inside it.
True Positives (TP) - True positives are the cases when the actual class of the data point was 1(True) and the
predicted is also 1(True).
Ex: The case where a person is actually having cancer(1) and the model classifying his case as cancer(1) comes under
True positive
True Negatives (TN) - True negatives are the cases when the actual class of the data point was 0(False) and the
predicted is also 0(False)
Ex: The case where a person NOT having cancer and the model classifying his case as Not cancer comes under True
Negatives.
6. Confusion Matrix
False Positives (FP) - False positives are the cases when the actual class of the data point was 0(False) and the
predicted is 1(True). False is because the model has predicted incorrectly and positive because
the class predicted was a positive one. (1)
Ex: A person NOT having cancer and the model classifying his case as cancer comes under False Positives.
False Negatives (FN) - False negatives are the cases when the actual class of the data point was 1(True) and the
predicted is 0(False). False is because the model has predicted incorrectly and negative
because the class predicted was a negative one. (0)
Ex: A person having cancer and the model classifying his case as No-cancer comes under False Negatives.
The ideal scenario that we all want is that the model should give 0 False Positives and 0 False Negatives.
But that’s not the case in real life as any model will NOT be 100% accurate most of the times.
7. Confusion Matrix
When to minimize what?
We know that there will be some error associated with every model that we use for predicting the true class of the
target variable. This will result in False Positives and False Negatives
There’s no hard rule that says what should be minimised in all the situations. It purely depends on the business
needs and the context of the problem you are trying to solve. Based on that, we might want to minimise either False
Positives or False negatives.
8. Confusion Matrix
When to minimize what?
Minimizing False Negatives
Let’s say in our cancer detection problem example, out of 100 people, only 5 people have cancer. In this case, we
want to correctly classify all the cancerous patients as even a very BAD model(Predicting everyone as NON-
Cancerous) will give us a 95% accuracy. But, in order to capture all cancer cases, we might end up making a
classification when the person actually NOT having cancer is classified as Cancerous. This might be okay as it is less
dangerous than NOT identifying/capturing a cancerous patient since we will anyway send the cancer cases for
further examination and reports. But missing a cancer patient will be a huge mistake as no further examination will
be done on them.
9. Confusion Matrix
When to minimize what?
Minimizing False Positives:
For better understanding of False Positives, let’s use a different example where the model classifies whether an
email is spam or not.
Let’s say that you are expecting an important email like hearing back from a recruiter or awaiting an admit letter
from a university. Let’s assign a label to the target variable and say,1: “Email is a spam” and 0:”Email is not a spam”.
Suppose the Model classifies that important email that you are desperately waiting for, as Spam(case of False
positive). Now, in this situation, this is pretty bad than classifying a spam email as important or not spam since in
that case, we can still go ahead and manually delete it and it’s not a pain if it happens once a while. So in case of
Spam email classification, minimising False positives is more important than False Negatives.
10. Performance Metrics
Different metrics used:
• Confusion Matrix
• Accuracy
• Precision
• Recall or Sensitivity
• Specificity
• F1 Score
• Log Loss
• Area under the curve (AUC)
• MAE – Mean Absolute Error
• MSE – Mean Squared Error
11. Accuracy
Accuracy in classification problems is the number of correct predictions made by the model over all kinds predictions
made.
In the Numerator, are our correct predictions (True positives and True Negatives) (marked as red in the fig above)
and in the denominator, are the kind of all predictions made by the algorithm (right as well as wrong ones).
12. Accuracy
When to use Accuracy:
Accuracy is a good measure when the target variable classes in the data are nearly balanced.
Eg: 60% classes in our fruits images data are apple and 40% are oranges.
A model which predicts whether a new image is Apple or an Orange, 97% of times correctly is a very good measure
in this example.
When not to use Accuracy:
Accuracy should never be used as a measure when the target variable classes in the data are a majority of one class.
Eg: In our cancer detection example with 100 people, only 5 people has cancer.
Let’s say our model is very bad and predicts every case as No Cancer. In doing so, it has classified those 95 non-
cancer patients correctly and 5 cancerous patients as Non-cancerous. Now even though the model is terrible at
predicting cancer, The accuracy of such a bad model is also 95%.
13. Performance Metrics
Different metrics used:
• Confusion Matrix
• Accuracy
• Precision
• Recall or Sensitivity
• Specificity
• F1 Score
• Log Loss
• Area under the curve (AUC)
• MAE – Mean Absolute Error
• MSE – Mean Squared Error
14. Precision
Precision is a measure that tells us what proportion of patients that we diagnosed as having cancer, actually had
cancer. The predicted positives (People predicted as cancerous are TP and FP) and the people actually having a
cancer are TP.
In our cancer example with 100 people, only 5 people have cancer. Let’s say our model is very bad and predicts every
case as Cancer. Since we are predicting everyone as having cancer, our denominator (True positives and False
Positives) is 100 and the numerator, person having cancer and the model predicting his case as cancer is 5. So in this
example, we can say that Precision of such model is 5%.
15. Performance Metrics
Different metrics used:
• Confusion Matrix
• Accuracy
• Precision
• Recall or Sensitivity
• Specificity
• F1 Score
• Log Loss
• Area under the curve (AUC)
• MAE – Mean Absolute Error
• MSE – Mean Squared Error
16. Recall or Sensitivity
Recall is a measure that tells us what proportion of patients that actually had cancer was diagnosed by the algorithm
as having cancer. The actual positives (People having cancer are TP and FN) and the people diagnosed by the model
having a cancer are TP. (Note: FN is included because the Person actually had a cancer even though the model
predicted otherwise).
Ex: In our cancer example with 100 people, 5 people actually have cancer. Let’s say that the model predicts every
case as cancer. So our denominator(True positives and False Negatives) is 5 and the numerator, person having cancer
and the model predicting his case as cancer is also 5(Since we predicted 5 cancer cases correctly). So in this example,
we can say that the Recall of such model is 100%. And Precision of such a model(As we saw above) is 5%
17. Accuracy
When to use Precision and when to you recall:
It is clear that recall gives us information about a classifier’s performance with respect to false negatives (how many
did we miss), while precision gives us information about its performance with respect to false positives(how many
did we caught)
Precision is about being precise. So even if there is only one cancer case, and we captured it correctly, then we are
100% precise.
Recall is not so much about capturing cases correctly but more about capturing all cases that have “cancer” with the
answer as “cancer”. So if we simply always say every case as “cancer”, we have 100% recall.
So basically if we want to focus more on minimising False Negatives, we would want our Recall to be as close to
100% as possible without precision being too bad and if we want to focus on minimising False positives, then our
focus should be to make Precision as close to 100% as possible.
18. Performance Metrics
Different metrics used:
• Confusion Matrix
• Accuracy
• Precision
• Recall or Sensitivity
• Specificity
• F1 Score
• Log Loss
• Area under the curve (AUC)
• MAE – Mean Absolute Error
• MSE – Mean Squared Error
19. Specificity
Specificity is a measure that tells us what proportion of patients that did NOT have cancer, were predicted by the
model as non-cancerous. The actual negatives (People actually NOT having cancer are FP and TN) and the people
diagnosed by us not having cancer are TN.
Specificity is the exact opposite of Recall.
Ex: In our cancer example with 100 people, 5 people actually have cancer. Let’s say that the model predicts every
case as cancer.
So our denominator(False positives and True Negatives) is 95 and the numerator, person not having cancer and the
model predicting his case as no cancer is 0 (Since we predicted every case as cancer). So in this example, we can that
that Specificity of such model is 0%.
20. Performance Metrics
Different metrics used:
• Confusion Matrix
• Accuracy
• Precision
• Recall or Sensitivity
• Specificity
• F1 Score
• Log Loss
• Area under the curve (AUC)
• MAE – Mean Absolute Error
• MSE – Mean Squared Error
21. F1 Score
We don’t really want to carry both Precision and Recall in our pockets every time we make a model for solving a
classification problem. So it’s best if we can get a single score that kind of represents both Precision(P) and Recall(R).
One way to do that is simply taking their arithmetic mean. i.e (P + R) / 2 where P is Precision and R is Recall. But
that’s pretty bad in some situations.
Why? - Suppose we have 100 credit card transactions, of which 97 are legit and 3 are fraud and let’s say we came up
a model that predicts everything as fraud. Precision and Recall for the example is shown in the fig below.
22. F1 Score
Now, if we simply take arithmetic mean of both, then it comes out to be nearly 51%. We shouldn’t be giving such a
moderate score to a terrible model since it’s just predicting every transaction as fraud.
So, we need something more balanced than the arithmetic mean and that is harmonic mean.
The Harmonic mean is given by the formula shown in the figure on the bottom right.
Harmonic mean is kind of an average when x and y are equal. But when x and y
are different, then it’s closer to the smaller number as compared to the larger
number.
For our previous example, F1 Score = Harmonic Mean(Precision, Recall)
F1 Score = 2 * Precision * Recall / (Precision + Recall) = 2*3*100/103 = 5%
So if one number is really small between precision and recall, the F1 Score kind of
raises a flag and is more closer to the smaller number than the bigger one, giving
the model an appropriate score rather than just an arithmetic mean.
23. Performance Metrics
Different metrics used:
• Confusion Matrix
• Accuracy
• Precision
• Recall or Sensitivity
• Specificity
• F1 Score
• Log Loss
• Area under the curve (AUC)
• MAE – Mean Absolute Error
• MSE – Mean Squared Error
24. Log Loss
Logarithmic Loss or Log Loss, works by penalising the false classifications.
It works well for multi-class classification. When working with Log Loss, the classifier must assign probability to each
class for all the samples.
Suppose, there are N samples belonging to M classes, then the Log Loss is calculated as below :
where,
y_ij, indicates whether sample i belongs to class j or not
p_ij, indicates the probability of sample i belonging to class j
Log Loss has no upper bound and it exists on the range [0, ∞). Log Loss nearer to 0 indicates higher accuracy,
whereas if the Log Loss is away from 0 then it indicates lower accuracy.
In general, minimising Log Loss gives greater accuracy for the classifier.
25. Performance Metrics
Different metrics used:
• Confusion Matrix
• Accuracy
• Precision
• Recall or Sensitivity
• Specificity
• F1 Score
• Log Loss
• Area under the curve (AUC)
• MAE – Mean Absolute Error
• MSE – Mean Squared Error
26. AUC – Area Under the ROC Curve
Idea of Thresholding:
Logistic regression returns a probability. You can use the returned probability "as is" (for example, the probability
that the user will click on this ad is 0.00023) or convert the returned probability to a binary value (for example, this
email is spam)
A logistic regression model that returns 0.9995 for a particular email message is predicting that it is very likely to be
spam. Conversely, another email message with a prediction score of 0.0003 on that same logistic regression model is
very likely not spam.
However, what about an email message with a prediction score of 0.6?
In order to map a logistic regression value to a binary category, you must define a classification threshold (also called
the decision threshold).
A value above that threshold indicates "spam"; a value below indicates "not spam." It is tempting to assume that the
classification threshold should always be 0.5, but thresholds are problem-dependent, and are therefore values that
you must tune.
27. AUC – Area Under the ROC Curve
ROC Curve
An ROC curve (receiver operating characteristic curve) is a graph showing the performance of a classification model
at all classification thresholds. This curve plots two parameters:
• True Positive Rate
• False Positive Rate
True Positive Rate (TPR) is a synonym for recall and is therefore defined as follows:
False Positive Rate (FPR) is defined as follows:
An ROC curve plots TPR vs. FPR at different classification thresholds.
(as shown in the right fig)
28. AUC – Area Under the ROC Curve
AUC Value
Lowering the classification threshold classifies more items as positive, thus increasing both False Positives and True
Positives.
To compute the points in an ROC curve, we could evaluate a logistic regression model many times with different
classification thresholds, but this would be inefficient. Fortunately, there's an efficient, sorting-based algorithm that
can provide this information for us, called AUC.
AUC stands for "Area under the ROC Curve." That is, AUC measures
the entire two-dimensional area underneath the entire
ROC curve (think integral calculus) from (0,0) to (1,1)
AUC provides an aggregate measure of performance across all
possible classification thresholds.
29. AUC – Area Under the ROC Curve
AUC Value
AUC ranges in value from 0 to 1. A model whose predictions are 100% wrong has an AUC of 0.0; one whose
predictions are 100% correct has an AUC of 1.0.
AUC is desirable for the following two reasons:
• AUC is scale-invariant. It measures how well predictions are ranked, rather than their absolute values.
• AUC is classification-threshold-invariant. It measures the quality of the model's predictions irrespective of what
classification threshold is chosen.
However, both these reasons come with caveats, which may limit the usefulness of AUC in certain use cases:
• Scale invariance is not always desirable. For example, sometimes we really do need well calibrated probability
outputs, and AUC won’t tell us about that.
• Classification-threshold invariance is not always desirable. In cases where there are wide disparities in the cost
of false negatives vs. false positives, it may be critical to minimize one type of classification error. For example,
when doing email spam detection, you likely want to prioritize minimizing false positives (even if that results in a
significant increase of false negatives). AUC isn't a useful metric for this type of optimization.
30. Performance Metrics
Different metrics used:
• Confusion Matrix
• Accuracy
• Precision
• Recall or Sensitivity
• Specificity
• F1 Score
• Log Loss
• Area under the curve (AUC)
• MAE – Mean Absolute Error
• MSE – Mean Squared Error
31. Mean Absolute Error
Mean Absolute Error is the average of the difference between the original values and the predicted values.
It gives us the measure of how far the predictions were from the actual output. However, they don’t gives us any
idea of the direction of the error i.e. whether we are under predicting the data or over predicting the data.
Mathematically, it is represented as :
32. Performance Metrics
Different metrics used:
• Confusion Matrix
• Accuracy
• Precision
• Recall or Sensitivity
• Specificity
• F1 Score
• Log Loss
• Area under the curve (AUC)
• MAE – Mean Absolute Error
• MSE – Mean Squared Error
33. Mean Squared Error
Mean Squared Error(MSE) is quite similar to Mean Absolute Error, the only difference being that MSE takes the
average of the square of the difference between the original values and the predicted values.
As, we take square of the error, the effect of larger errors become more pronounced then smaller error, hence the
model can now focus more on the larger errors.
Instead of MSE, we generally use RMSE, which is equal to the square root of MSE.
Taking the square root of the average squared errors has some interesting implications for RMSE. Since the errors are
squared before they are averaged, the RMSE gives a relatively high weight to large errors.
This means the RMSE should be more useful when large errors are particularly undesirable.
34. Mean Squared Error
The three tables below show examples where MAE is steady and RMSE increases as the variance associated with the
frequency distribution of error magnitudes also increases.
35. Mean Squared Error
Conclusion:
1. RMSE has the benefit of penalizing large errors more so can be more appropriate in some cases, for example, if
being off by 10 is more than twice as bad as being off by 5. But if being off by 10 is just twice as bad as being off
by 5, then MAE is more appropriate.
2. From an interpretation standpoint, MAE is clearly the winner. RMSE does not describe average error alone and
has other implications that are more difficult to tease out and understand.
3. One distinct advantage of RMSE over MAE is that RMSE avoids the use of taking the absolute value, which is
undesirable in many mathematical calculations