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Machine Learning: Algorithms & Applications
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Module 7
Tree-Based Models: From Decision Trees to Random Forests
1
Decision Trees: Construction, Splitting, and Pruning
Explain the decision tree algorithm, including splitting criteria (Gini impurity, information gain), tree construction, and pruning.
2
Decision Tree Pros and Cons
Identify the strengths and weaknesses of decision trees, such as their interpretability and tendency to overfit.
Decision Trees: Classification, Visualization, and Evaluation
Apply a decision tree classifier to a dataset, visualize the resulting tree, and evaluate its performance.
4
Ensemble Learning: Bagging for Variance Reduction
Explain the concept of ensemble learning and the 'bagging' strategy for reducing model variance.
5
Random Forests: From Bagging to Better Generalization
Explain how a Random Forest model evolves from bagged decision trees and why it typically improves generalization.
Random Forest vs. Decision Tree: A Performance Comparison
Apply a Random Forest classifier to a dataset and compare its performance with a single decision tree.
Random Forest Feature Importance
Extract and interpret feature importances from a trained Random Forest model.
Previous module
Foundational Classification Algorithms
Next module
Support Vector Machines for Classification