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ML_Supervised
·
Module 8
Boosting Algorithms from Scratch
1
Decision Stumps as Weak Learners
Implement a decision stump (a one-level decision tree) as a weak learner.
2
Building AdaBoost with Weighted Decision Stumps
Implement the AdaBoost algorithm by sequentially training weighted decision stumps.
3
Calculating Pseudo-Residuals for Regression Loss Functions
Calculate pseudo-residuals for a regression loss function (e.g., MSE).
4
Building a Gradient Boosting Regressor
Implement a Gradient Boosting Regressor by iteratively fitting decision trees to pseudo-residuals.
Gradient Boosting for Binary Classification
Adapt the Gradient Boosting algorithm for binary classification tasks.
Previous module
Decision Trees and Random Forests from Scratch
Next module
Neural Network Foundations