Скачать или смотреть Feature Selection Methods in Machine Learning Filter, Wrapper & Embedded Techniques Explained

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In this complete Machine Learning tutorial in Urdu/Hindi, we’ll dive deep into one of the most crucial steps in building accurate and efficient ML models — Feature Selection.

Feature Selection is the process of choosing the most important input variables that have a real impact on the output prediction. By removing irrelevant or redundant features, we improve model performance, reduce overfitting, and make our ML pipeline faster and more interpretable.

In this video, you’ll learn Feature Selection Methods in Machine Learning — explained step by step with examples and code using Python and Scikit-learn.

🔍 What You’ll Learn in This Video:

✅ What is Feature Selection and why it’s important for every ML model
✅ Difference between Feature Selection and Feature Extraction
✅ Types of Feature Selection Methods:
 1️⃣ Filter Methods – based on statistical scores like ANOVA F-Test, Chi-Square, Mutual Information
 2️⃣ Wrapper Methods – like Recursive Feature Elimination (RFE) and RFECV with Cross Validation
 3️⃣ Embedded Methods – using algorithms such as Random Forest and Decision Tree for feature importance

✅ Practical implementation using Scikit-learn:
 - SelectKBest with F-Classif
 - Chi2 for categorical data
 - Mutual Info Classifier for nonlinear relations
 - Recursive Feature Elimination (RFE)
 - RFECV for automated optimal feature selection
 - SelectFromModel using Tree-Based models

✅ Comparison of methods: Filter vs Wrapper vs Embedded
✅ Advantages and Disadvantages of each method
✅ Best practices for Feature Selection in Machine Learning projects
✅ How to avoid common mistakes like data leakage and overfitting
✅ Step-by-step workflow for real projects

🧩 Why This Video Is Important

Whether you’re a beginner starting your ML journey or an intermediate learner looking to optimize your models, Feature Selection is a skill that separates good data scientists from great ones.

By the end of this tutorial, you’ll be able to confidently:

Identify the best features from your dataset

Reduce computational cost

Improve model accuracy and interpretability

Build scalable ML pipelines using Filter, Wrapper, and Embedded methods

💡 Tools & Libraries Used

Python 🐍

Scikit-learn (sklearn.feature_selection)

Pandas & NumPy

Jupyter Notebook


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