Decision Trees,Resampling & Evaluation Metrics : ONE-SHOT GATE DA | Concept to Combat Session 4 🔥

Описание: Welcome to Session 4 of the Concept to Combat series!

This is your ultimate one-stop resource for mastering some of the most high-weight and frequently tested topics in the GATE DA Machine Learning syllabus: Decision Trees, Resampling Methods, and Evaluation Metrics.

What you'll learn:
In this session, we will do a recap of the concepts and then go in deep dive for extensive practice of GATE-level problems. We don’t just stop at the theory; we ensure you can apply every formula and geometric intuition to solve complex questions under pressure.

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ML Module Schedule: Jan 20 – Feb 1. Subscribe to TAAI- Manoj Kumar to join the combat live!

Jump to Topics:

[00:00:00] Session Introduction and Overview
[00:01:50] Soft Margin SVM: Transition from Hard Margin
[00:05:32] Understanding the Slack Variable (Zeta)
[00:09:27] The C Hyperparameter and Misclassification Trade-offs
[00:17:21] SVM Regularization: Bias-Variance Trade-off
[00:24:35] Introduction to Hinge Loss
[00:27:36] Log Loss vs. Hinge Loss Comparison
[00:33:12] Concept Check: SVM Practice Questions
[00:39:37] Introduction to Decision Trees and Uncertainty
[00:42:39] Mathematical Definition of Entropy
[00:47:55] Information Gain and Feature Selection
[00:58:24] Constructing a Decision Tree: Root Node Selection
[01:09:54] Gini Index and Gini Impurity
[01:21:51] Handling Continuous Features (Cut Points)
[01:25:53] Tree Depth and Overfitting Risks
[01:42:25] Resampling Overview: Training, Validation, and Test Sets
[01:47:13] Hyperparameter Tuning with Validation Data
[01:57:21] Leave-One-Out Cross-Validation (LOOCV)
[02:02:29] K-Fold Cross-Validation: Rationale and Implementation
[02:07:11] Practice Problem 1: Solving for 3-Fold CV with Ridge Regression candidates.
[02:09:45] Technicality: Determining the number of rows in Training Matrix $X$ per fold.
[02:11:35] Practice Problem 2: Calculating total model iterations (LOOCV vs. 10-Fold CV).
[02:14:02] Concept Review: Why we discard intermediate models and retrain on full data.
[02:16:30] Practice Problem 3: MCQ on selecting the final model and estimating generalization error.
[02:21:40] Deep Dive: Nested Cross-Validation for Hyperparameter tuning.
[02:24:15] Step-by-Step Calculation: How 10 outer folds and 5 inner folds result in 261 iterations.
[02:27:50] Practice Problem 4: Identifying the equivalence of LOOCV and M-Fold CV.
[02:30:15] Practice Problem 5: Calculating Training Set Error for Linear SVM.
[02:33:45] Visual Analysis: Calculating LOOCV Error for SVM decision boundaries.
[02:38:20] Case Study: Decision Tree Splitting on real-valued attributes.
[02:42:15] Comparison: Training Set Error vs. LOOCV Error for DT2 algorithms.
[02:45:30] Final Summary of Resampling Methods for GATE DA.
[02:47:11] Why Accuracy is Insufficient for Imbalanced Data
[02:51:28] Confusion Matrix: TP, TN, FP, and FN
[02:52:57] Precision and Recall Formulas
[02:57:40] Use Case Analysis (Surveillance vs. Content Filtering)
[03:07:11] ROC Curve and Area Under Curve (AUC)
[03:15:51] Analyzing ROC Curve Behavior
[03:37:57] R-Squared (R2): Explained Variability
[03:44:12] Adjusted R-Squared: Penalty for Irrelevant Features


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✅ Complete syllabus coverage for GATE DA.
✅ Concept-focused lectures + regular doubt sessions.
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