How to transform and select variables/features when creating a predictive model using machine learning. To see the source code visit https://github.com/Davisy/Feature-Engineering-and-Feature-Selection
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.
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.
This document provides an overview of machine learning concepts including:
- The differences between deep learning, neural networks, machine learning, and artificial intelligence.
- Examples of machine learning applications such as image classification, text summarization, and fraud detection.
- The main types of machine learning including supervised, unsupervised, semi-supervised, and reinforcement learning.
- Common challenges in machine learning like bad data, overfitting, and underfitting models.
- Methods for evaluating machine learning models like validation sets and cross-validation.
The document describes two feature extraction methods: attention based and statistics based. The attention based method models how human vision finds salient regions using an architecture that decomposes images into channels and creates image pyramids, then combines the information to generate saliency maps. This method was applied to face recognition but had problems with pose and expression changes. The statistics based method aims to select a subset of important features using criteria based on how well the features represent the original data.
1. Autoencoders are unsupervised neural networks that are useful for dimensionality reduction and clustering. They learn an efficient coding of the input in an unsupervised manner.
2. Deep autoencoders, also known as stacked autoencoders, are autoencoders with multiple hidden layers that can learn hierarchical representations of the data. They are trained layer-by-layer to learn increasingly higher level features.
3. Variational autoencoders are a type of autoencoder that are probabilistic models, with the encoder output being the parameters of an assumed distribution such as Gaussian. They can generate new samples from the learned distribution.
Machine Learning: Applications, Process and TechniquesRui Pedro Paiva
Machine learning can be applied across many domains such as business, entertainment, medicine, and software engineering. The document outlines the machine learning process which includes data collection, feature extraction, model learning, and evaluation. It also provides examples of machine learning applications in various domains, such as using decision trees to make credit decisions in business, classifying emotions in music for playlist generation in entertainment, and detecting heart murmurs from audio data in medicine.
Winning data science competitions, presented by Owen ZhangVivian S. Zhang
<featured> Meetup event hosted by NYC Open Data Meetup, NYC Data Science Academy. Speaker: Owen Zhang, Event Info: http://www.meetup.com/NYC-Open-Data/events/219370251/
This document discusses and provides examples of supervised and unsupervised learning. Supervised learning involves using labeled training data to learn relationships between inputs and outputs and make predictions. An example is using data on patients' attributes to predict the likelihood of a heart attack. Unsupervised learning involves discovering hidden patterns in unlabeled data by grouping or clustering items with similar attributes, like grouping fruits by color without labels. The goal of supervised learning is to build models that can make predictions when new examples are presented.
Active learning is a machine learning technique where the learner is able to interactively query the oracle (e.g. a human) to obtain labels for new data points in an effort to learn more accurately from fewer labeled examples. The learner selects the most informative samples to be labeled by the oracle, such as samples closest to the decision boundary or where models disagree most. This allows the learner to minimize the number of labeled samples needed, thus reducing the cost of training an accurate model. Suggested improvements include querying batches of samples instead of single samples and accounting for varying labeling costs.
Introduction to Recurrent Neural NetworkKnoldus Inc.
The document provides an introduction to recurrent neural networks (RNNs). It discusses how RNNs differ from feedforward neural networks in that they have internal memory and can use their output from the previous time step as input. This allows RNNs to process sequential data like time series. The document outlines some common RNN types and explains the vanishing gradient problem that can occur in RNNs due to multiplication of small gradient values over many time steps. It discusses solutions to this problem like LSTMs and techniques like weight initialization and gradient clipping.
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 document summarizes a machine learning project for Homesite to predict customer quote conversions. The team members are Jack, Harry, and Abhishek. Homesite wants to predict the likelihood of customers purchasing insurance contracts based on their quote. The training data has 261k rows and 298 predictors, while the test data has 200k rows and the same 298 columns. Some key steps included data cleaning, using gradient boosting and random forests, and calculating the AUC (area under the ROC curve) metric to evaluate model performance. The team's model achieved an AUC of 0.95, indicating it does not overfit and has little bias.
Data preprocessing is the process of preparing raw data for analysis by cleaning it, transforming it, and reducing it. The key steps in data preprocessing include data cleaning to handle missing values, outliers, and noise; data transformation techniques like normalization, discretization, and feature extraction; and data reduction methods like dimensionality reduction and sampling. Preprocessing ensures the data is consistent, accurate and suitable for building machine learning models.
Data Science, Machine Learning and Neural NetworksBICA Labs
Lecture briefly overviewing state of the art of Data Science, Machine Learning and Neural Networks. Covers main Artificial Intelligence technologies, Data Science algorithms, Neural network architectures and cloud computing facilities enabling the whole stack.
The document discusses machine learning concepts including modeling, evaluation, model selection, training models, and addressing issues like overfitting and underfitting. It explains that modeling tries to emulate human learning through mathematical and statistical formulations. Evaluation methods like holdout, k-fold cross-validation, and leave-one-out cross-validation are used to select models and train them on datasets while avoiding overfitting or underfitting issues. Parametric models have fixed parameters while non-parametric models are based on training data.
Machine learning is a method of data analysis that uses algorithms to iteratively learn from data without being explicitly programmed. It allows computers to find hidden insights in data and become better at tasks via experience. Machine learning has many practical applications and is important due to growing data availability, cheaper and more powerful computation, and affordable storage. It is used in fields like finance, healthcare, marketing and transportation. The main approaches are supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. Each has real-world examples like loan prediction, market basket analysis, webpage classification, and marketing campaign optimization.
The document introduces data preprocessing techniques for data mining. It discusses why data preprocessing is important due to real-world data often being dirty, incomplete, noisy, inconsistent or duplicate. It then describes common data types and quality issues like missing values, noise, outliers and duplicates. The major tasks of data preprocessing are outlined as data cleaning, integration, transformation and reduction. Specific techniques for handling missing values, noise, outliers and duplicates are also summarized.
The document discusses hyperparameters and hyperparameter tuning in deep learning models. It defines hyperparameters as parameters that govern how the model parameters (weights and biases) are determined during training, in contrast to model parameters which are learned from the training data. Important hyperparameters include the learning rate, number of layers and units, and activation functions. The goal of training is for the model to perform optimally on unseen test data. Model selection, such as through cross-validation, is used to select the optimal hyperparameters. Training, validation, and test sets are also discussed, with the validation set used for model selection and the test set providing an unbiased evaluation of the fully trained model.
Feature engineering is an important step in machine learning that involves transforming raw data into features better suited for building models. It includes techniques like feature selection, extraction, transformation, encoding, and augmentation. Feature selection involves choosing the most relevant existing features, while extraction creates new features from existing ones. The goal is to improve model performance by reducing noise and bias from irrelevant or redundant features.
This document discusses five ways to attain optimal model complexity in machine learning: 1) feature engineering and selection to optimize variables, 2) data augmentation to expand datasets, 3) dimensionality reduction to reduce high-dimensional data, 4) active learning where algorithms query users to label data, and 5) ensemble models that combine multiple models to improve performance over single models. These techniques help improve model performance, efficiency, and ability to learn from data.
This document discusses feature engineering, which is the process of transforming raw data into features that better represent the underlying problem for predictive models. It covers feature engineering categories like feature selection, feature transformation, and feature extraction. Specific techniques covered include imputation, handling outliers, binning, log transforms, scaling, and feature subset selection methods like filter, wrapper, and embedded methods. The goal of feature engineering is to improve machine learning model performance by preparing proper input data compatible with algorithm requirements.
What is Feature Engineering?
Feature engineering is the process of creating or selecting relevant
features from raw data to improve the performance of machine
learning models.
Feature engineering is the process of transforming raw data into
features that are suitable for machine learning models. In other
words, it is the process of selecting, extracting, and transforming the
most relevant features from the available data to build more accurate
and efficient machine learning models.
In the context of machine learning, features are individual measurable
properties or characteristics of the data that are used as inputs for the
learning algorithms. The goal of feature engineering is to transform the
raw data into a suitable format that captures the underlying patterns
and relationships in the data, thereby enabling the machine learning
model to make accurate predictions or classifications
Experimental Design for Distributed Machine Learning with Myles BakerDatabricks
This document discusses experimental design for distributed machine learning models. It outlines common problems in machine learning modeling like selecting the best algorithm and evaluating a model's expected generalization error. It describes steps in a machine learning study like collecting data, building models, and designing experiments. The goal of experimentation is to understand how model factors affect outcomes and obtain statistically significant conclusions. Techniques discussed for analyzing distributed model outputs include precision-recall curves, confusion matrices, and hypothesis testing methods like the chi-squared test and McNemar's test. The document emphasizes that experimental design for distributed learning poses new challenges around data characteristics, computational complexity, and reproducing results across models.
Statistical theory is a branch of mathematics and statistics that provides the foundation for understanding and working with data, making inferences, and drawing conclusions from observed phenomena. It encompasses a wide range of concepts, principles, and techniques for analyzing and interpreting data in a systematic and rigorous manner. Statistical theory is fundamental to various fields, including science, social science, economics, engineering, and more.
Machine learning lets you make better business decisions by uncovering patterns in your consumer behavior data that is hard for the human eye to spot. You can also use it to automate routine, expensive human tasks that were previously not doable by computers. In the business to business space (B2B), if your competitors can make wiser business decisions based on data and automate more business operations but you still base your decisions on guesswork and lack automation, you will lose out on business productivity. In this introduction to machine learning tech talk, you will learn how to use machine learning even if you do not have deep technical expertise on this technology.
Topics covered:
1.What is machine learning
2.What is a typical ML application architecture
3.How to start ML development with free resource links
4.Key decision factors in ML technology selection depending on use case scenarios
Machine Learning: Transforming Data into Insightspemac73062
This presentation, titled "Machine Learning: Transforming Data into Insights," offers an in-depth exploration of Machine Learning (ML), highlighting its critical role in modern technology and various industries. The presentation begins with a thorough introduction to ML, distinguishing it from traditional programming and emphasizing its importance in today's data-driven world. It then categorizes ML into three main types: Supervised, Unsupervised, and Reinforcement Learning, providing examples and use cases for each.
The ML workflow is meticulously detailed, covering every stage from data collection and preparation to model training, evaluation, and deployment. Emphasis is placed on the importance of high-quality data, effective data preprocessing techniques, and the selection of appropriate algorithms. The presentation also explores common challenges in ML, such as overfitting, underfitting, data privacy concerns, and the interpretability of complex models.
By providing a holistic view of ML, including its practical applications, technical workflow, challenges, and ethical implications, this presentation aims to educate and inspire the audience, highlighting the profound impact of ML on data analysis and decision-making processes in various fields.
Dataset: Gather a large dataset of laptops and their features, including processor speed, RAM, storage, and display size, along with their corresponding prices.
Feature engineering: Extracting meaningful features from the dataset, such as brand, model, and year, and transforming them into a format that machine learning algorithms can use.
Model selection: Choosing the most appropriate machine learning algorithm, such as linear regression, decision tree, or random forest, based on the type of data and desired level of accuracy.
Model training: Splitting the dataset into training and testing sets, and using the training data to train the machine learning model.
Model evaluation: Testing the model's performance on the testing data and evaluating its accuracy using metrics such as mean squared error or R-squared.
Hyperparameter tuning: Optimizing the model's hyperparameters, such as learning rate or regularization strength, to achieve the best performance.
Mahout is an Apache project that provides scalable machine learning libraries for Java. It contains algorithms for classification, clustering, and recommendation engines that can operate on huge datasets using distributed computing. Some key algorithms in Mahout include Naive Bayes classification, k-means clustering, and item-based recommenders. Classification with Mahout involves training a model on labeled historical data, evaluating the model on test data, and then using the model to classify new unlabeled data at scale. Feature selection and representation are important for building an accurate classification model in Mahout.
Building a performing Machine Learning model from A to ZCharles Vestur
A 1-hour read to become highly knowledgeable about Machine learning and the machinery underneath, from scratch!
A presentation introducing to all fundamental concepts of Machine Learning step by step, following a classical approach to build a performing model. Simple examples and illustrations are used all along the presentation to make the concepts easier to grasp.
AI/ML Infra Meetup | ML explainability in MichelangeloAlluxio, Inc.
AI/ML Infra Meetup
May. 23, 2024
Organized by Alluxio
For more Alluxio Events: https://www.alluxio.io/events/
Speaker:
- Eric Wang (Software Engineer, @Uber)
Uber has numerous deep learning models, most of which are highly complex with many layers and a vast number of features. Understanding how these models work is challenging and demands significant resources to experiment with various training algorithms and feature sets. With ML explainability, the ML team aims to bring transparency to these models, helping to clarify their predictions and behavior. This transparency also assists the operations and legal teams in explaining the reasons behind specific prediction outcomes.
In this talk, Eric Wang will discuss the methods Uber used for explaining deep learning models and how we integrated these methods into the Uber AI Michelangelo ecosystem to support offline explaining.
Agile analytics : An exploratory study of technical complexity managementAgnirudra Sikdar
The thesis involved the reviewing of various case studies to determine the types of modelling, choice of algorithm, types of analytical approaches and trying to determine the various complexities arising from these cases. From these reviews, procedures have been proposed to improve the efficiency and manage the various types of complexities from using agile methodological perspective. Focus was mostly done on Customer Segmentation and Clustering , with the sole purpose to bridge Big Data and Business Intelligence together using Analytic.
The Power of Auto ML and How Does it WorkIvo Andreev
Automated ML is an approach to minimize the need of data science effort by enabling domain experts to build ML models without having deep knowledge of algorithms, mathematics or programming skills. The mechanism works by allowing end-users to simply provide data and the system automatically does the rest by determining approach to perform particular ML task. At first this may sound discouraging to those aiming to the “sexiest job of the 21st century” - the data scientists. However, Auto ML should be considered as democratization of ML, rather that automatic data science.
In this session we will talk about how Auto ML works, how is it implemented by Microsoft and how it could improve the productivity of even professional data scientists.
Algorithm ExampleFor the following taskUse the random module .docxdaniahendric
Algorithm Example
For the following task:
Use the random module to write a number guessing game.
The number the computer chooses should change each time you run the program.
Repeatedly ask the user for a number. If the number is different from the computer's let the user know if they guessed too high or too low. If the number matches the computer's, the user wins.
Keep track of the number of tries it takes the user to guess it.
An appropriate algorithm might be:
Import the random module
Display a welcome message to the user
Choose a random number between 1 and 100
Get a guess from the user
Set a number of tries to 0
As long as their guess isn’t the number
Check if guess is lower than computer
If so, print a lower message.
Otherwise, is it higher?
If so, print a higher message.
Get another guess
Increment the tries
Repeat
When they guess the computer's number, display the number and their tries count
Notice that each line in the algorithm corresponds to roughly a line of code in Python, but there is no coding itself in the algorithm. Rather the algorithm lays out what needs to happen step by step to achieve the program.
Software Quality Metrics for Object-Oriented Environments
AUTHORS:
Dr. Linda H. Rosenberg Lawrence E. Hyatt
Unisys Government Systems Software Assurance Technology Center
Goddard Space Flight Center Goddard Space Flight Center
Bld 6 Code 300.1 Bld 6 Code 302
Greenbelt, MD 20771 USA Greenbelt, MD 20771 USA
I. INTRODUCTION
Object-oriented design and development are popular concepts in today’s software development
environment. They are often heralded as the silver bullet for solving software problems. While
in reality there is no silver bullet, object-oriented development has proved its value for systems
that must be maintained and modified. Object-oriented software development requires a
different approach from more traditional functional decomposition and data flow development
methods. This includes the software metrics used to evaluate object-oriented software.
The concepts of software metrics are well established, and many metrics relating to product
quality have been developed and used. With object-oriented analysis and design methodologies
gaining popularity, it is time to start investigating object-oriented metrics with respect to
software quality. We are interested in the answer to the following questions:
• What concepts and structures in object-oriented design affect the quality of the
software?
• Can traditional metrics measure the critical object-oriented structures?
• If so, are the threshold values for the metrics the same for object-oriented designs as for
functional/data designs?
• Which of the many new metrics found in the literature are useful to measure the critical
concepts of object-oriented structures?
II. METRIC EVALUATION CRITERIA
While metrics for the traditional functional decomposition and data analysis design appro ...
AI-Assisted Feature Selection for Big Data ModelingDatabricks
The number of features going into models is growing at an exponential rate thanks to the power of Spark. So is the number of models each company is creating. The common approach is to throw as many features as you can into a model.
Machine learning can be used to predict whether a user will purchase a book on an online book store. Features about the user, book, and user-book interactions can be generated and used in a machine learning model. A multi-stage modeling approach could first predict if a user will view a book, and then predict if they will purchase it, with the predicted view probability as an additional feature. Decision trees, logistic regression, or other classification algorithms could be used to build models at each stage. This approach aims to leverage user data to provide personalized book recommendations.
Principal Component Analysis (PCA) is an unsupervised learning algorithm used for dimensionality reduction. It transforms correlated variables into linearly uncorrelated variables called principal components. PCA works by considering the variance of each attribute to reduce dimensionality while preserving as much information as possible. It is commonly used for exploratory data analysis, predictive modeling, and visualization.
This research paper proposes a machine learning approach to identify fake reviews using supervised learning techniques. It focuses on detecting fake reviews in e-commerce systems, specifically using features extracted from the reviews themselves as well as behavioral features of the reviewers. The paper experiments with various classifiers on a real Yelp dataset, finding that a KNN classifier with K=7 achieves the best performance, and that including behavioral features improves results. The proposed approach demonstrates an effective way of identifying fake reviews through text analysis and reviewer behavior analysis using machine learning.
The Hilarious Saga of Ships Losing Their Voices: these gigantic vessels that rule the seas can't even keep track of themselves without our help. When their beloved AIS system fails, they're rendered blind, deaf and dumb - a cruel joke on their supposed maritime prowess.
This document, in its grand ambition, seeks to dissect the marvel that is maritime open-source intelligence (maritime OSINT). Real-world case studies will be presented with the gravitas of a Shakespearean tragedy, illustrating the practical applications and undeniable benefits of maritime OSINT in various security scenarios.
For the cybersecurity professionals and maritime law enforcement authorities, this document will be nothing short of a revelation, equipping them with the knowledge and tools to navigate the complexities of maritime OSINT operations while maintaining a veneer of ethical and legal propriety. Researchers, policymakers, and industry stakeholders will find this document to be an indispensable resource, shedding light on the potential and implications of maritime OSINT in safeguarding our seas and ensuring maritime security and safety.
-------------------------
This document aims to provide a comprehensive analysis of maritime open-source intelligence (maritime OSINT) and its various aspects: examining the ethical implications of employing maritime OSINT techniques, particularly in the context of maritime law enforcement authorities, identifying and addressing the operational challenges faced by maritime law enforcement authorities when utilizing maritime OSINT, such as data acquisition, analysis, and dissemination.
The analysis will offer a thorough and insightful examination of these aspects, providing a valuable resource for cybersecurity professionals, law enforcement agencies, maritime industry stakeholders, and researchers alike. Additionally, the document will serve as a valuable resource for researchers, policymakers, and industry stakeholders seeking to understand the potential and implications of maritime OSINT in ensuring maritime security and safety.
Maritime Open-Source Intelligence (OSINT) refers to the practice of gathering and analyzing publicly available information related to maritime activities, vessels, ports, and other maritime infrastructure for intelligence purposes. It involves leveraging various open-source data sources and tools to monitor, track, and gain insights into maritime operations, potential threats, and anomalies. Maritime Open-Source Intelligence (OSINT) is crucial for capturing information critical to business operations, especially when electronic systems like Automatic Identification Systems (AIS) fail. OSINT can provide valuable context and insights into vessel operations, including the identification of vessels, their positions, courses, and speeds
A. Data Sources
• Vessel tracking websites and services (e.g., MarineTraffic, VesselFinder) that provide real-time and historical data on ship movements, positions, and d
Selling software today doesn’t look anything like it did a few years ago. Especially software that runs inside a customer environment. Dreamfactory has used Anchore and Ask Sage to achieve compliance in a record time. Reducing attack surface to keep vulnerability counts low, and configuring automation to meet those compliance requirements. After achieving compliance, they are keeping up to date with Anchore Enterprise in their CI/CD pipelines.
The CEO of Ask Sage, Nic Chaillan, the CEO of Dreamfactory Terence Bennet, and Anchore’s VP of Security Josh Bressers are going to discuss these hard problems.
In this webinar we will cover:
- The standards Dreamfactory decided to use for their compliance efforts
- How Dreamfactory used Ask Sage to collect and write up their evidence
- How Dreamfactory used Anchore Enterprise to help achieve their compliance needs
- How Dreamfactory is using automation to stay in compliance continuously
- How reducing attack surface can lower vulnerability findings
- How you can apply these principles in your own environment
When you do security right, they won’t know you’ve done anything at all!
UiPath Community Day Amsterdam: Code, Collaborate, ConnectUiPathCommunity
Welcome to our third live UiPath Community Day Amsterdam! Come join us for a half-day of networking and UiPath Platform deep-dives, for devs and non-devs alike, in the middle of summer ☀.
📕 Agenda:
12:30 Welcome Coffee/Light Lunch ☕
13:00 Event opening speech
Ebert Knol, Managing Partner, Tacstone Technology
Jonathan Smith, UiPath MVP, RPA Lead, Ciphix
Cristina Vidu, Senior Marketing Manager, UiPath Community EMEA
Dion Mes, Principal Sales Engineer, UiPath
13:15 ASML: RPA as Tactical Automation
Tactical robotic process automation for solving short-term challenges, while establishing standard and re-usable interfaces that fit IT's long-term goals and objectives.
Yannic Suurmeijer, System Architect, ASML
13:30 PostNL: an insight into RPA at PostNL
Showcasing the solutions our automations have provided, the challenges we’ve faced, and the best practices we’ve developed to support our logistics operations.
Leonard Renne, RPA Developer, PostNL
13:45 Break (30')
14:15 Breakout Sessions: Round 1
Modern Document Understanding in the cloud platform: AI-driven UiPath Document Understanding
Mike Bos, Senior Automation Developer, Tacstone Technology
Process Orchestration: scale up and have your Robots work in harmony
Jon Smith, UiPath MVP, RPA Lead, Ciphix
UiPath Integration Service: connect applications, leverage prebuilt connectors, and set up customer connectors
Johans Brink, CTO, MvR digital workforce
15:00 Breakout Sessions: Round 2
Automation, and GenAI: practical use cases for value generation
Thomas Janssen, UiPath MVP, Senior Automation Developer, Automation Heroes
Human in the Loop/Action Center
Dion Mes, Principal Sales Engineer @UiPath
Improving development with coded workflows
Idris Janszen, Technical Consultant, Ilionx
15:45 End remarks
16:00 Community fun games, sharing knowledge, drinks, and bites 🍻
Airports, banks, stock exchanges, and countless other critical operations got thrown into chaos!
In an unprecedented event, a recent CrowdStrike update had caused a global IT meltdown, leading to widespread Blue Screen of Death (BSOD) errors, and crippling 8.5 million Microsoft Windows systems.
What triggered this massive disruption? How did Microsoft step in to provide a lifeline? And what are the next steps for recovery?
Swipe to uncover the full story, including expert insights and recovery steps for those affected.
Securiport Gambia is a civil aviation and intelligent immigration solutions provider founded in 2001. The company was created to address security needs unique to today’s age of advanced technology and security threats. Securiport Gambia partners with governments, coming alongside their border security to create and implement the right solutions.
TrustArc Webinar - Innovating with TRUSTe Responsible AI CertificationTrustArc
In a landmark year marked by significant AI advancements, it’s vital to prioritize transparency, accountability, and respect for privacy rights with your AI innovation.
Learn how to navigate the shifting AI landscape with our innovative solution TRUSTe Responsible AI Certification, the first AI certification designed for data protection and privacy. Crafted by a team with 10,000+ privacy certifications issued, this framework integrated industry standards and laws for responsible AI governance.
This webinar will review:
- How compliance can play a role in the development and deployment of AI systems
- How to model trust and transparency across products and services
- How to save time and work smarter in understanding regulatory obligations, including AI
- How to operationalize and deploy AI governance best practices in your organization
Leading Bigcommerce Development Services for Online RetailersSynapseIndia
As a leading provider of Bigcommerce development services, we specialize in creating powerful, user-friendly e-commerce solutions. Our services help online retailers increase sales and improve customer satisfaction.
Increase Quality with User Access Policies - July 2024Peter Caitens
⭐️ Increase Quality with User Access Policies ⭐️, presented by Peter Caitens and Adam Best of Salesforce. View the slides from this session to hear all about “User Access Policies” and how they can help you onboard users faster with greater quality.
1. Feature Engineering & Feature
Selection
Davis David
Data Scientist at ParrotAI
d.david@parrotai.co.tz
2. CONTENT:
1. Feature Engineering
2. Missing Data
3. Continuous Features
4. Categorical Features
5. Feature Selection
6. Practical Feature Engineering and Selection
3. 1.Feature Engineering
Feature engineering refers to a process of selecting and transforming
variables/features when creating a predictive model using machine
learning.
Feature engineering has two goals:
● Preparing the proper input dataset, compatible with the machine
learning algorithm requirements.
● Improving the performance of machine learning models.
5. 57% of data scientists regard cleaning and organizing data as the least
enjoyable part of their work
6. “At the end of the day, some machine learning projects succeed
and some fail. What makes the difference? Easily the most
important factor is the features used.”
— Prof. Pedro Domingos from University of Washington
Read his paper here : A few useful things to know about machine learning
7. 2. Missing Data
Handling missing data is important as many machine learning
algorithms do not support data with missing values.
Having missing values in the dataset can cause errors and poor
performance with some machine learning algorithms.
9. 2. How to handle Missing Values
(a) Variable Deletion
Variable deletion involves dropping variables(columns) with missing values on
an case by case basis.
This method makes sense when lot of missing values in a variable and if the
variable is of relatively less importance.
The only case that it may worth deleting a variable is when its missing values
are more than 60% of the observations.
10. 2. How to handle Missing Values
(a) Variable Deletion
11. 2. How to handle Missing Values
(b) Mean or Median Imputation
A common technique is to use the mean or median of the non-missing
observations.
This strategy can be applied on a feature which has numeric data.
12. 2. How to handle Missing Values
(c) Most Common Value
Replacing the missing values with the maximum occurred value in a column/feature is a
good option for handling categorical columns/features.
13. 3. Continuous Features
● Continuous features in the dataset have different range of values.
● If you train your model with different range of value the model will not
perform well.
Example continuous features: age, salary , prices, heights
Common methods
Min-Max Normalization
Standardization
14. 3. Continuous Features
(a) Min-Max Normalization
For each value in a feature, Min-Max normalization subtracts the minimum
value in the feature and then divides by the range. The range is the difference
between the original maximum and original minimum.
It scale all values in a fixed range between 0 and 1.
15. 3. Continuous Features
(b) Standardization
The Standardization ensures that for each feature have the mean is 0 and the
variance is 1, bringing all features to the same magnitude.
If the standard deviation of features is different, their range also would differ
from each other.
x = observation, μ = mean , σ = standard deviation
16. 4.Categorical Features
Categorical features represents types of data which may be divided into groups.
Example: genders, educational levels
Any non-numerical values need to be converted to integers or floats in order to
be utilized in most machine learning libraries.
Common Methods
one-hot-encoding(Dummy variables)
Label Encoding
17. 4.Categorical Features
(a) One-hot-encoding
By far the most common way to represent categorical variables is using
the one-hot encoding or one-out-of-N encoding, also known as dummy
variables.
The idea behind dummy variables is to replace a categorical variable
with one or more new features that can have the values 0 and 1.
19. 4.Categorical Features
(b) Label Encoding
Label encoding is simply converting each categorical value in a column to a
number.
NB: It is recommended to use label encoding to a Binary variable
20. 5.Feature Selection
● Feature Selection is the process where you automatically or manually select
those features which contribute most to your prediction variable or output in
which you are interested in.
● Having irrelevant features in your data can decrease the accuracy of the
models and make your model learn based on irrelevant features..
21. 5.Feature Selection
Top reasons to use feature selection are:
● It enables the machine learning algorithm to train faster.
● It reduces the complexity of a model and makes it easier to interpret.
● It improves the accuracy of a model if the right subset is chosen.
● It reduces overfitting.
22. 5. Feature Selection
“I prepared a model by selecting all the features and I got an accuracy of around 65%
which is not pretty good for a predictive model and after doing some feature
selection and feature engineering without doing any logical changes in my model
code my accuracy jumped to 81% which is quite impressive”
- By Raheel Shaikh
23. 5.Feature Selection
(a) Univariate Selection
● Statistical tests can be used to select those independent features that have
the strongest relationship with the target feature in your dataset.
E.g. Chi squared test
● The scikit-learn library provides the SelectKBest class that can be used with a
suite of different statistical tests to select a specific number of features.
24. 5.Feature Selection
(b) Feature Importance
Feature importance gives you a score for each feature of your data, the higher
the score more important or relevant is the feature towards your target feature.
Feature importance is an inbuilt class that comes with Tree Based Classifiers
Example:
Random Forest Classifiers
Extra Tree Classifiers
25. 5. Feature Selection
(c) Correlation Matrix with Heatmap
● Correlation show how the features are related to each other or the target
feature.
● Correlation can be positive (increase in one value of feature increases the
value of the target variable) or negative (increase in one value of feature
decreases the value of the target variable)