Feature Selection Techniques in Python | Chapter 8 Machine Learning Tutorial скачать в хорошем качестве

Feature Selection Techniques in Python | Chapter 8 Machine Learning Tutorial

Welcome to Chapter 8 of our Machine Learning tutorial series using Scikit-Learn. In this video, we focus on **feature selection techniques**, an important step in building efficient and accurate machine learning models. Feature selection helps reduce noise, improve model performance, and decrease training time by keeping only the most relevant features in a dataset. Topics covered in this chapter include: 1. Understanding Feature Selection What feature selection is and why it matters in machine learning. Difference between feature selection and dimensionality reduction. How irrelevant and redundant features can negatively affect model accuracy. 2. SelectKBest Learn how *SelectKBest* selects the top features based on statistical scores. Understand how to choose the value of K and interpret feature scores. Practical examples using classification datasets. 3. Mutual Information for Feature Selection Learn how *mutual information* measures the dependency between features and target variables. Understand how *mutual_info_classif* works for classification problems. Explore real-world use cases where mutual information is effective. 4. VarianceThreshold Learn how *VarianceThreshold* removes low-variance features. Understand why features with little or no variance provide minimal information to models. Practical examples of removing constant and near-constant features. 5. Practical Examples Apply feature selection techniques on real datasets. Compare model performance before and after feature selection. 6. Best Practices How to choose the right feature selection technique based on your dataset and model type. Common mistakes to avoid during feature selection. By the end of this chapter, you will understand how to apply different feature selection techniques using Scikit-Learn, reduce dataset complexity, and improve machine learning model performance through effective feature selection. Useful Links: GitHub: https://github.com/Ezee-Kits/ YouTube:    / @ezee_kits   Email: ezeekits@gmail.com #Python #MachineLearning #ScikitLearn #DataScience #PythonTutorial #LearnPython #FeatureSelection #SelectKBest #MutualInformation #VarianceThreshold #MLForBeginners #PythonProgramming #AI #DataAnalysis #DataVisualization #MLTutorial #PythonProjects #SoftwareDevelopment #Tech #PredictiveModeling