This Logistic Regression Presentation will help you understand how a Logistic Regression algorithm works in Machine Learning. In this tutorial video, you will learn what is Supervised Learning, what is Classification problem and some associated algorithms, what is Logistic Regression, how it works with simple examples, the maths behind Logistic Regression, how it is different from Linear Regression and Logistic Regression applications. At the end, you will also see an interesting demo in Python on how to predict the number present in an image using Logistic Regression.
Below topics are covered in this Machine Learning Algorithms Presentation:
1. What is supervised learning?
2. What is classification? what are some of its solutions?
3. What is logistic regression?
4. Comparing linear and logistic regression
5. Logistic regression applications
6. Use case - Predicting the number in an image
What is Machine Learning: Machine Learning is an application of Artificial Intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.
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About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning.
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Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
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What skills will you learn from this Machine Learning course?
By the end of this Machine Learning course, you will be able to:
1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, naive bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems
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In machine learning, support vector machines (SVMs, also support vector networks[1]) are supervised learning models with associated learning algorithms that analyze data and recognize patterns, used for classification and regression analysis. The basic SVM takes a set of input data and predicts, for each given input, which of two possible classes forms the output, making it a non-probabilistic binary linear classifier.
This document summarizes support vector machines (SVMs), a machine learning technique for classification and regression. SVMs find the optimal separating hyperplane that maximizes the margin between positive and negative examples in the training data. This is achieved by solving a convex optimization problem that minimizes a quadratic function under linear constraints. SVMs can perform non-linear classification by implicitly mapping inputs into a higher-dimensional feature space using kernel functions. They have applications in areas like text categorization due to their ability to handle high-dimensional sparse data.
Support Vector Machine (SVM) is a supervised machine learning algorithm that can be used for both classification and regression analysis. It works by finding a hyperplane in an N-dimensional space that distinctly classifies the data points. SVM selects the hyperplane that has the largest distance to the nearest training data points of any class, since larger the margin lower the generalization error of the classifier. SVM can efficiently perform nonlinear classification by implicitly mapping their inputs into high-dimensional feature spaces.
Linear Regression Analysis | Linear Regression in Python | Machine Learning A...Simplilearn
This Linear Regression in Machine Learning Presentation will help you understand the basics of Linear Regression algorithm - what is Linear Regression, why is it needed and how Simple Linear Regression works with solved examples, Linear regression analysis, applications of Linear Regression and Multiple Linear Regression model. At the end, we will implement a use case on profit estimation of companies using Linear Regression in Python. This Machine Learning presentation is ideal for beginners who want to understand Data Science algorithms as well as Machine Learning algorithms.
Below topics are covered in this Linear Regression Machine Learning Tutorial:
1. Introduction to Machine Learning
2. Machine Learning Algorithms
3. Applications of Linear Regression
4. Understanding Linear Regression
5. Multiple Linear Regression
6. Use case - Profit estimation of companies
What is Machine Learning: Machine Learning is an application of Artificial Intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.
- - - - - - - -
About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning.
- - - - - - -
Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
- - - - - - -
Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
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Who should take this Machine Learning Training Course?
We recommend this Machine Learning training course for the following professionals in particular:
1. Developers aspiring to be a data scientist or Machine Learning engineer
2. Information architects who want to gain expertise in Machine Learning algorithms
3. Analytics professionals who want to work in Machine Learning or artificial intelligence
4. Graduates looking to build a career in data science and Machine Learning
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This presentation is aimed at fitting a Simple Linear Regression model in a Python program. IDE used is Spyder. Screenshots from a working example are used for demonstration.
Logistic regression in Machine LearningKuppusamy P
Logistic regression is a predictive analysis algorithm that can be used for classification problems. It estimates the probabilities of different classes using the logistic function, which outputs values between 0 and 1. Logistic regression transforms its output using the sigmoid function to return a probability value. It is used for problems like email spam detection, fraud detection, and tumor classification. The independent variables should be independent of each other and the dependent variable must be categorical. Gradient descent is used to minimize the loss function and optimize the model parameters during training.
Scikit-Learn is a powerful machine learning library implemented in Python with numeric and scientific computing powerhouses Numpy, Scipy, and matplotlib for extremely fast analysis of small to medium sized data sets. It is open source, commercially usable and contains many modern machine learning algorithms for classification, regression, clustering, feature extraction, and optimization. For this reason Scikit-Learn is often the first tool in a Data Scientists toolkit for machine learning of incoming data sets.
The purpose of this one day course is to serve as an introduction to Machine Learning with Scikit-Learn. We will explore several clustering, classification, and regression algorithms for a variety of machine learning tasks and learn how to implement these tasks with our data using Scikit-Learn and Python. In particular, we will structure our machine learning models as though we were producing a data product, an actionable model that can be used in larger programs or algorithms; rather than as simply a research or investigation methodology.
Support Vector Machine - How Support Vector Machine works | SVM in Machine Le...Simplilearn
This document discusses support vector machines (SVM) and provides an example of using SVM for classification. It begins with common applications of SVM like face detection and image classification. It then provides an overview of SVM, explaining how it finds the optimal separating hyperplane between two classes by maximizing the margin between them. An example demonstrates SVM by classifying people as male or female based on height and weight data. It also discusses how kernels can be used to handle non-linearly separable data. The document concludes by showing an implementation of SVM on a zoos dataset to classify animals as crocodiles or alligators.
Logistic regression : Use Case | Background | Advantages | DisadvantagesRajat Sharma
This slide will help you to understand the working of logistic regression which is a type of machine learning model along with use cases, pros and cons.
1. Machine learning involves developing algorithms that can learn from data and improve their performance over time without being explicitly programmed. 2. Neural networks are a type of machine learning algorithm inspired by the human brain that can perform both supervised and unsupervised learning tasks. 3. Supervised learning involves using labeled training data to infer a function that maps inputs to outputs, while unsupervised learning involves discovering hidden patterns in unlabeled data through techniques like clustering.
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.
Principal Component Analysis, or PCA, is a factual method that permits you to sum up the data contained in enormous information tables by methods for a littler arrangement of "synopsis files" that can be all the more handily envisioned and broke down.
Basic of Decision Tree Learning. This slide includes definition of decision tree, basic example, basic construction of a decision tree, mathlab example
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.
K-Folds cross-validation is one method that attempts to maximize the use of the available data for training and then testing a model. It is particularly useful for assessing model performance, as it provides a range of accuracy scores across (somewhat) different data sets.
Ensemble Learning is a technique that creates multiple models and then combines them to produce improved results.
Ensemble learning usually produces more accurate solutions than a single model would.
The document presents a machine learning presentation by five students. It discusses key machine learning concepts including supervised learning (classification and regression), unsupervised learning (clustering and association), semi-supervised learning, and reinforcement learning. Examples of applications are provided. The differences between traditional computer science programs and machine learning programs are outlined. The future of machine learning is predicted to include its integration into all AI systems, machine learning-as-a-service, continuously learning connected systems, and hardware enhancements to support machine learning capabilities.
Logistic regression is a machine learning classification algorithm that predicts the probability of a categorical dependent variable. It models the probability of the dependent variable being in one of two possible categories, as a function of the independent variables. The model transforms the linear combination of the independent variables using the logistic sigmoid function to output a probability between 0 and 1. Logistic regression is optimized using maximum likelihood estimation to find the coefficients that maximize the probability of the observed outcomes in the training data. Like linear regression, it makes assumptions about the data being binary classified with no noise or highly correlated independent variables.
Logistic regression is a machine learning classification algorithm used to predict the probability of a categorical dependent variable given one or more independent variables. It uses a logit link function to transform the probability values into odds ratios between 0 and infinity. The model is trained by minimizing a cost function called logistic loss using gradient descent optimization. Model performance is evaluated using metrics like accuracy, precision, recall, and the confusion matrix, and can be optimized by adjusting the probability threshold for classifications.
Logistic regression estimates the probability of an event occurring based on independent variables. It is used when the dependent variable is binary or categorical. The logistic function transforms the probability to a value between 0 and 1. Maximum likelihood estimation is used to find the parameter estimates that maximize the likelihood of obtaining the observed sample data.
Logistic regression vs. logistic classifier. History of the confusion and the...Adrian Olszewski
Despite the wrong (yet widespread) claim, that "logistic regression is not a regression", it's one of the key regression tool in experimental research, like the clinical trials. It is used also for advanced testing hypotheses.
The logistic regression is part of the GLM (Generalized Linear Model) regression framework. I expanded this topic here: https://medium.com/@r.clin.res/is-logistic-regression-a-regression-46dcce4945dd
This document discusses machine learning algorithms for classification problems, specifically logistic regression. It explains that logistic regression predicts the probability of a binary outcome using a sigmoid function. Unlike linear regression, which is used for continuous outputs, logistic regression is used for classification problems where the output is discrete/categorical. It describes how logistic regression learns model parameters through gradient descent optimization of a likelihood function to minimize error. Regularization techniques can also be used to address overfitting issues that may arise from limited training data.
This document provides guidance on performing and interpreting logistic regression analyses in SPSS. It discusses selecting appropriate statistical tests based on variable types and study objectives. It covers assumptions of logistic regression like linear relationships between predictors and the logit of the outcome. It also explains maximum likelihood estimation, interpreting coefficients, and evaluating model fit and accuracy. Guidelines are provided on reporting logistic regression results from SPSS outputs.
This document provides an overview of machine learning and logistic regression. It discusses key concepts in machine learning like representation, evaluation, and optimization. It also discusses different machine learning algorithms like decision trees, neural networks, and support vector machines. The document then focuses on logistic regression, explaining concepts like maximum likelihood estimation, concordance, and confusion matrices which are used to evaluate logistic regression models. It provides an example of using logistic regression for a banking customer classification problem to predict defaults.
These slides will help you to crack interviews for product-based companies if you are planning your career in Data Science, Artificial Intelligence, etc.
These slides cover machine learning models more specifically classification algorithms (Logistic Regression, Linear Discriminant Analysis (LDA),
K-Nearest Neighbors (KNN),
Trees, Random Forests, and Boosting
Support Vector Machines (SVM),
Neural Networks)
Regression analysis mathematically and statistically describes the relationship between a set of independent variables and a dependent variable. This presentation describes the concept of regression and its types with suitable illustrations. This presentation also explains the regression analysis spss path and its interpretations.
The document summarizes an Analytics Vidhya meetup event. It discusses that the meetups will occur once a month, with the next one on May 24th. It aims to provide networking and learning around data science, big data, machine learning and IoT. It introduces the volunteer organizers and outlines the agenda, which includes an introduction, discussing the model building lifecycle, data exploration techniques, and modeling techniques like logistic regression, decision trees, random forests, and SVMs. It provides details on practicing these techniques by predicting survival on the Titanic dataset.
This document discusses key statistical concepts including random variables, probability distributions, expected value, variance, and correlation. It defines discrete and continuous random variables and explains how probability distributions assign probabilities to the possible values of a random variable. It also defines important metrics like expected value and variance, and how they are calculated for discrete and continuous random variables. The document concludes by explaining correlation, how the correlation coefficient measures the strength and direction of linear association between two variables, and how it is calculated.
Lecture 4 - Linear Regression, a lecture in subject module Statistical & Mach...Maninda Edirisooriya
Simplest Machine Learning algorithm or one of the most fundamental Statistical Learning technique is Linear Regression. This was one of the lectures of a full course I taught in University of Moratuwa, Sri Lanka on 2023 second half of the year.
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.
This document discusses supervised learning. Supervised learning uses labeled training data to train models to predict outputs for new data. Examples given include weather prediction apps, spam filters, and Netflix recommendations. Supervised learning algorithms are selected based on whether the target variable is categorical or continuous. Classification algorithms are used when the target is categorical while regression is used for continuous targets. Common regression algorithms discussed include linear regression, logistic regression, ridge regression, lasso regression, and elastic net. Metrics for evaluating supervised learning models include accuracy, R-squared, adjusted R-squared, mean squared error, and coefficients/p-values. The document also covers challenges like overfitting and regularization techniques to address it.
This document discusses regression analysis techniques. It begins with defining regression and its objectives, such as using independent variables to predict dependent variable values. It then covers understanding regression through layman terms and statistical terms. The rest of the document assesses goodness of fit both graphically and statistically. It discusses assumptions of regression like normality, equal variance, and independent errors. It also covers analyzing residuals, outliers, influential cases, and addressing issues like multicollinearity.
This document provides an introduction to mathematical modeling. It defines mathematical models as simplified representations of real-world entities that are characterized by variables, parameters, and functional forms. The modeling process involves gathering real-world data, simplifying it, developing a mathematical model, solving the model, and verifying its accuracy. Difference equations are used to model change over discrete time periods, while differential equations model continuous change. Examples are provided of modeling population growth and interacting species through systems of difference equations.
Introduction to correlation and regression analysisFarzad Javidanrad
This document provides an introduction to correlation and regression analysis. It defines key concepts like variables, random variables, and probability distributions. It discusses how correlation measures the strength and direction of a linear relationship between two variables. Correlation coefficients range from -1 to 1, with values closer to these extremes indicating stronger correlation. The document also introduces determination coefficients, which measure the proportion of variance in one variable explained by the other. Regression analysis builds on correlation to study and predict the average value of one variable based on the values of other explanatory variables.
Genetic algorithms are a type of evolutionary algorithm that mimics natural selection. They operate on a population of potential solutions applying operators like selection, crossover and mutation to produce the next generation. The algorithm iterates until a termination condition is met, such as a solution being found or a maximum number of generations being produced. Genetic algorithms are useful for optimization and search problems as they can handle large, complex search spaces. However, they require properly defining the fitness function and tuning various parameters like population size, mutation rate and crossover rate.
This document discusses linear regression analysis. It defines linear regression and its key assumptions. Regression is used to estimate the relationship between a dependent and independent variable. The document provides an example of a company using regression to analyze the relationship between sales and man-hours. Calculations are shown to estimate the regression line and measure how much of the variation in sales is explained by variation in man-hours. Key outputs like the coefficient of determination and correlation coefficient are also introduced.
The slides include a condensed explanation of Transformers and their advantages in compared to CNN and RNN. The presentation begins with a brief explanation of Transformers. Then, the advantages and disadvantages of Transformers relative to CNNs and RNNs are discussed. The attention mechanism is next presented, followed by an illustration of the structure of the document "Attention all you need."
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Dear Sakthi Thiru Dr. G. B. Senthil Kumar,
It is with great honor and respect that we extend this formal invitation to you. As a distinguished leader whose presence commands admiration and reverence, we cordially invite you to join us in celebrating the 25th anniversary of our graduation from Adhiparasakthi Engineering College on 27th July, 2024. we would be honored to have you by our side as we reflect on the achievements and memories of the past 25 years.
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To improve the quality of our business we have to supervise all the operations and tasks. We can do different quality checks before the product is put to the market. We can do all these activities in a single module that is the Quality module in Odoo 17. This slide will show how to use the quality module in odoo 17.
How to Restrict Price Modification to Managers in Odoo 17 POSCeline George
This slide will represent the price control functionality in Odoo 17 PoS module. This feature provides the opportunity to restrict price adjustments. We can limit pricing changes to managers exclusively with it.
How to Configure Field Cleaning Rules in Odoo 17Celine George
In this slide let’s discuss how to configure field cleaning rules in odoo 17. Field Cleaning is used to format the data that we use inside Odoo. Odoo 17's Data Cleaning module offers Field Cleaning Rules to improve data consistency and quality within specific fields of our Odoo records. By using the field cleaning, we can correct the typos, correct the spaces between them and also formats can be corrected.
How to Load Custom Field to POS in Odoo 17 - Odoo 17 SlidesCeline George
This slide explains how to load custom fields you've created into the Odoo 17 Point-of-Sale (POS) interface. This approach involves extending the functionalities of existing POS models (e.g., product.product) to include your custom field.
How to Integrate Facebook in Odoo 17 - Odoo 17 SlidesCeline George
Integrating Facebook with other platforms, such as business software like Odoo, serves several purposes and can offer numerous benefits depending on the specific goals of your business.
What is the Use of API.onchange in Odoo 17Celine George
The @api.onchange decorator in Odoo is indeed used to trigger a method when a field's value changes. It's commonly used for validating data or triggering actions based on the change of a specific field. When the field value changes, the function decorated with @api.onchange will be called automatically.
Odoo 17 Project Module : New Features - Odoo 17 SlidesCeline George
The Project Management module undergoes significant enhancements, aimed at providing users with more robust tools for planning, organizing, and executing projects effectively.
Plato and Aristotle's Views on Poetry by V.Jesinthal Maryjessintv
PPT on Plato and Aristotle's Views on Poetry prepared by Mrs.V.Jesinthal Mary, Dept of English and Foreign Languages(EFL),SRMIST Science and Humanities ,Ramapuram,Chennai-600089
Life of Ah Gong and Ah Kim ~ A Story with Life Lessons (Hokkien, English & Ch...OH TEIK BIN
A PowerPoint Presentation of a fictitious story that imparts Life Lessons on loving-kindness, virtue, compassion and wisdom.
The texts are in Romanized Hokkien, English and Chinese.
For the Video Presentation with audio narration in Hokkien, please check out the Link:
https://vimeo.com/manage/videos/987932748
How to Manage Advanced Pricelist in Odoo 17Celine George
Maintaining relationships with customers is important for a business. Customizing prices will help to maintain the relationships with customers. Odoo provides a pricing strategy called pricelists. We can set appropriate prices for the clients. And advanced price rules will help to set prices based on different conditions. This slide will show how to manage advanced pricelists in odoo 17.
How to Configure Extra Steps During Checkout in Odoo 17 Website AppCeline George
Odoo websites allow us to add an extra step during the checkout process to collect additional information from customers. This can be useful for gathering details that aren't necessarily covered by standard shipping and billing addresses.
4. Surviving the Titanic
• ID
• Survived
• Class
• Name
• Sex
• Age
• Siblings
• Parents/children abroad
• Ticket
• Fare
• Cabin
• Place of Embarkment
Teaching the model with the
passenger dataset
Dropping the non-essential
components of the dataset
Determining the survival of
passengers and evaluating the
model
5. Agenda
What is Supervised Learning?
What is Classification? What are some of its solutions?
What is Logistic Regression?
Comparing Linear and Logistic Regression
Logistic Regression applications
Use Case – Predicting the number in an image
7. What is Supervised Learning?
That’s an
apple!
apple
Teacher teaches child Child recognizes an apple when she sees it again
A model is able to make predictions based on past data
8. Where does Logistic Regression fit it?
Machine Learning
Supervised Learning Unsupervised Learning
AssociationClusteringClassification Regression
The systems predicts future outcomes based on training from past input
10. A few Classification Solutions
A B
We take decisions using a tree structure. Each
branch node represents a choice, and leaf node
represents a decision
Decision Trees
11. A few Classification Solutions
A B
Decision Trees
We take decisions using a tree structure. Each
branch node represents a choice, and leaf node
represents a decision
It helps determine what the given object is, based on
its similarity to the objects it is compared toK=3
K=7
K-Nearest Neighbor
12. It helps determine what the given object is, based on
its similarity to the objects it is compared toK=3
K=7
K-Nearest Neighbor
Decision Trees
A few Classification Solutions
A B
We determine the probability of an event occurring
with the help of a tree structure
A dataset with one or more independent variables is
used to determine binary output of the dependent
variable
Logistic Regression
14. What is Logistic Regression?
Imagine it’s been a few years since
you serviced your car.
One day you wonder…
15. What is Logistic Regression?
It is a classification algorithm, used to predict binary outcomes for a given set of independent
variables. The dependent variable’s outcome is discrete.
Regression model created based on other
users’ experience
0.60
0.20
0.40
0.80
1.00
1 2 3 4 5 6
Years since service
Probabilityofbreakdown
How long until the
car breaks down?
You provide years since
last service
16. What is Logistic Regression?
Probability>0.50
Value rounded off to
1 : The car will
breakdown
Probability<0.50
Value rounded off to 0:
The car will not
breakdown
Here, the threshold
value 0.50 indicates
that the car is more
likely to breakdown
after 3.5 years of
usage
Model makes predictions
0.60
0.20
0.40
0.80
1.00
1 2 3 4 5 6
Years since last service
Probabilityofbreakdown
0.50
0.29
0.90
Threshold Value
18. Linear Regression
It is a statistical method that helps find the relationship between an independent and dependent variable,
both of which are continuous
He performed
well in the last
quarter. How
much raise
should he get?
19. Linear Regression
41 2 3 5
5
10
15
20
25
Employee rating
Salaryhike
Collection of ratings and corresponding
hikes Linear Regression is performed on data
The management provides the
corresponding salary hike
Employee rating
20. Linear and Logistic Regression
Here’s the graph of how linear
regression would be, for a given
scenario
x
y
21. Linear and Logistic Regression
What if you wanted to know whether the
employee would get a promotion or not
based on their rating
41 2 3 5
0 =No
1=Yes
Employee rating
Probabilityofgettinga
promotion
22. Linear and Logistic Regression
This graph would not be able to
make such a prediction. So we clip
the line at 0 and 1.
41 2 3 5
Employee rating
0 =No
1=Yes
Probabilityofgettinga
promotion
23. Linear and Logistic Regression
So, how did this… …become this?
41 2 3 5
Employee rating
41 2 3 5
Employee rating
0 =No
1=Yes
Probabilityofgettinga
promotion
0 =No
1=Yes
Probabilityofgettinga
promotion
24. The Math behind Logistic Regression
To understand Logistic Regression, let’s talk
about the odds of success
Odds (𝜃) =
Probability of an
event happening
Probability of an
event not
happening
or 𝜃 =
𝑝
1 − 𝑝
The values of odds range from 0 to ∞
The values of probability change from 0 to 1
25. The Math behind Logistic Regression
Type equation here.
Take the equation of the straight line
𝛽0
x
y
Here, 𝛽0 is the y-intercept
𝛽1 is the slope of the line
x is the value of the x co-ordinate
y is the value of the prediction
The equation would be: 𝑦 = 𝛽0 + 𝛽1 𝑥
26. The Math behind Logistic Regression
Type equation here.
Now, we predict the odds of success
e𝑙𝑛
𝑝 𝑥
1 − 𝑝 𝑥
= e 𝛽0+𝛽1 𝑥
log
𝑝 𝑥
1−𝑃 𝑥
= 𝛽0 + 𝛽1 𝑥
Exponentiating both sides:
𝑝 𝑥
1 − 𝑝 𝑥
= e 𝛽0+𝛽1 𝑥
Let Y = e 𝛽0+𝛽1 𝑥
Then
𝑝 𝑥
1−𝑝 𝑥
= Y
𝑝 𝑥 = 𝑌 1 − 𝑝 𝑥
𝑝 𝑥 = 𝑌 − 𝑌 𝑝 𝑥
𝑝 𝑥 + 𝑌 𝑝 𝑥 = 𝑌
𝑝 𝑥 1 + 𝑌 = 𝑌
𝑝 𝑥 =
𝑌
1 + 𝑌
𝑝 𝑥 =
e 𝛽0+𝛽1 𝑥
1 + e 𝛽0+𝛽1 𝑥
The equation of a sigmoid function:
𝑝 𝑥 =
e 𝛽0+𝛽1 𝑥
1 + e 𝛽0+𝛽1 𝑥
𝑝 𝑥 =
1
1 + e−(𝛽0+𝛽1 𝑥)
27. The Math behind Logistic Regression
Type equation here.
Now, we predict the odds of success
e𝑙𝑛
𝑝 𝑥
1 − 𝑝 𝑥
= e 𝛽0+𝛽1 𝑥
log
𝑝 𝑥
1−𝑃 𝑥
= 𝛽0 + 𝛽1 𝑥
Exponentiating both sides:
𝑝 𝑥
1 − 𝑝 𝑥
= e 𝛽0+𝛽1 𝑥
Let Y = e 𝛽0+𝛽1 𝑥
Then
𝑝 𝑥
1−𝑝 𝑥
= Y
𝑝 𝑥 = 𝑌 1 − 𝑝 𝑥
𝑝 𝑥 = 𝑌 − 𝑌 𝑝 𝑥
𝑝 𝑥 + 𝑌 𝑝 𝑥 = 𝑌
𝑝 𝑥 1 + 𝑌 = 𝑌
𝑝 𝑥 =
𝑌
1 + 𝑌
𝑝 𝑥 =
e 𝛽0+𝛽1 𝑥
1 + e 𝛽0+𝛽1 𝑥
The equation of a sigmoid function:
𝑝 𝑥 =
e 𝛽0+𝛽1 𝑥
1 + e 𝛽0+𝛽1 𝑥
𝑝 𝑥 =
1
1 + e−(𝛽0+𝛽1 𝑥)
41 2 3 5
0
0.25
0.50
0.75
1
Employee rating
Probabilityofgettinga
promotion
A sigmoid curve is obtained!
29. How is Linear and Logistic Regression different?
Linear Regression Logistic Regression
• Used to solve Regression
Problems
30. How is Linear and Logistic Regression different?
Linear Regression Logistic Regression
• Used to solve Classification
Problems
• Used to solve Regression
Problems
31. How is Linear and Logistic Regression different?
Linear Regression Logistic Regression
• Used to solve Classification
Problems
• Used to solve Regression
Problems
• The response variables are
continuous in nature
32. How is Linear and Logistic Regression different?
Linear Regression Logistic Regression
• Used to solve Classification
Problems
• The response variable is
categorical in nature
• Used to solve Regression
Problems
• The response variables are
continuous in nature
33. How is Linear and Logistic Regression different?
Linear Regression Logistic Regression
• Used to solve Classification
Problems
• The response variable is
categorical in nature
• Used to solve Regression
Problems
• The response variables are
continuous in nature
• It helps estimate the dependent
variable when there is a change
in the independent variable.
34. How is Linear and Logistic Regression different?
Linear Regression Logistic Regression
• Used to solve Classification
Problems
• The response variable is
categorical in nature
• It helps calculate the possibility
of a particular event taking
place.
• Used to solve Regression
Problems
• The response variables are
continuous in nature
• It helps estimate the dependent
variable when there is a change
in the independent variable.
35. How is Linear and Logistic Regression different?
Linear Regression Logistic Regression
• Used to solve Classification
Problems
• The response variable is
categorical in nature
• It helps calculate the possibility
of a particular event taking
place.
• Used to solve Regression
Problems
• The response variables are
continuous in nature
• It helps estimate the dependent
variable when there is a change
in the independent variable.
• Is a straight line.
36. How is Linear and Logistic Regression different?
Linear Regression Logistic Regression
• Used to solve Classification
Problems
• The response variable is
categorical in nature
• It helps calculate the possibility
of a particular event taking
place.
• An S-curve. (S = Sigmoid)
• Used to solve Regression
Problems
• The response variables are
continuous in nature
• It helps estimate the dependent
variable when there is a change
in the independent variable.
• Is a straight line.
38. Identifies the different components
that are present in the image, and
helps categorize them
Logistic Regression Applications
Humans Animals Vehicles
Image Categorization
39. Determines the possibility of patient
survival, taking age, ISS and RTS into
consideration
Logistic Regression Applications
Healthcare (TRISS)
Patient survival %
Revised
Trauma Score
Injury Severity
Score
Age
41. Use Case – Predicting numbers
Can you guess
what number I am? Are you a 3? 4?
I don’t know!
8x8 image
42. Use Case – Predicting numbers
Dividing the data set
Training
set
Test set
The model being trained
Model identifies number in
images
Test set applied
A number 4
A number 1
43. Use Case – Implementation
Importing libraries and their associated methods
Determining the total number of images and labels
44. Use Case – Implementation
Displaying some of the images and labels
45. Use Case – Implementation
Dividing dataset into Training and Test set
46. Use Case – Implementation
Import the Logistic Regression model
Making an instance of the model and training it
Predicting the output of the first element of the test set
Predicting the output of the first 10 elements of the test set
47. Use Case – Implementation
Predicting for the entire dataset
Determining the accuracy of the model
Representing the confusion matrix in a heat map
48. Use Case – Implementation
Accurately predicting the image to
contain a zero
Inaccurately predicting the image to
contain a seven
49. Use Case – Implementation
Presenting predictions and actual output
50. Use Case – Predicting numbers
Dividing the data set
Training
set
Test
set
The model being trained Model identifies number in
images
Test set applied
A number 4
A number 2