/courses/machine-learning/trees-and-ensembles/Recent content in Trees & Ensembles on DATATWEETSHugoenCopyright (c) 2025 DatatweetsFri, 14 Nov 2025 09:00:00 +0200/courses/machine-learning/trees-and-ensembles/lesson-1-foundations-of-decision-trees/Fri, 14 Nov 2025 09:00:00 +0200/courses/machine-learning/trees-and-ensembles/lesson-1-foundations-of-decision-trees/Learn the building blocks of decision trees: nodes, splits, and leaves. See how a tree asks yes/no questions, how Gini and entropy measure node impurity, and how the best split is chosen to reduce that impurity. You will explore the real Adult Income dataset and read a clear depth-3 tree./courses/machine-learning/trees-and-ensembles/lesson-2-building-decision-trees-with-scikit-learn/Fri, 14 Nov 2025 09:00:00 +0200/courses/machine-learning/trees-and-ensembles/lesson-2-building-decision-trees-with-scikit-learn/Learn the full scikit-learn DecisionTreeClassifier workflow: encode categorical columns with get_dummies, split into train and test sets, fit and score a tree, and read which features it relied on. You will train a depth-limited tree on the real Adult Income dataset and reach about 85 percent test accuracy./courses/machine-learning/trees-and-ensembles/lesson-3-evaluating-and-optimizing-decision-trees/Fri, 14 Nov 2025 09:00:00 +0200/courses/machine-learning/trees-and-ensembles/lesson-3-evaluating-and-optimizing-decision-trees/Understand overfitting in decision trees: see how a deep tree memorizes the training data, plot train versus test accuracy across depths, and learn the pruning hyperparameters that keep a tree honest on unseen data./courses/machine-learning/trees-and-ensembles/lesson-4-cross-validation-and-ensemble-methods/Fri, 14 Nov 2025 09:00:00 +0200/courses/machine-learning/trees-and-ensembles/lesson-4-cross-validation-and-ensemble-methods/Learn why a single train/test split can mislead you, and use k-fold cross-validation for trustworthy accuracy estimates. Then meet ensemble methods: how bagging and random forests average many de-correlated trees to build a stronger classifier on the real Adult Income dataset./courses/machine-learning/trees-and-ensembles/lesson-5-guided-project-predicting-employee-productivity/Fri, 14 Nov 2025 09:00:00 +0200/courses/machine-learning/trees-and-ensembles/lesson-5-guided-project-predicting-employee-productivity/Apply everything you have learned about trees and ensembles in a complete, hands-on regression project. You will load, clean, and encode the real Garment Productivity dataset, fit a decision tree and a random forest to predict actual productivity, read feature importances, and reason about why the model’s R-squared is modest and how you might improve it.