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Master advanced ML/DS statistics
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Module 7
Tree-Based Models and Ensembles
1
Decision Trees: Classification & Regression
Build and interpret a decision tree for a classification or regression task.
2
Understanding Split Criteria: Gini Impurity & Information Gain
Explain split criteria such as Gini impurity or information gain.
3
Bagging: Reducing Variance in Ensemble Models
Describe how bootstrap aggregating (bagging) reduces variance in ensemble models.
4
Random Forests: Enhancing Robustness through Feature Randomness
Apply Random Forests and explain how feature randomness improves model robustness.
5
Gradient Boosting: Sequential Error Reduction
Explain the sequential nature of gradient boosting for error reduction.
6
Applying Gradient Boosting Models
Apply a gradient boosting model (e.g., XGBoost, LightGBM) to a dataset.
7
Random Forests vs. Gradient Boosting: A Comparative Analysis
Compare the strengths and weaknesses of Random Forests and Gradient Boosting.
8
Interpreting Tree Model Feature Importance
Interpret feature importance scores generated by tree-based models.
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Classification Models and Evaluation
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Unsupervised Learning: Dimensionality Reduction and Clustering