Random Forests Explained | Ensemble Learning & Classification with Python

How can combining multiple Decision Trees produce a more stable and powerful Machine Learning model? In this practical class, we break down Random Forests from the intuition behind ensemble learning to building and evaluating a Random Forest model using Python. You will learn why a single Decision Tree can be sensitive to changes in training data and how Random Forests address this through bootstrap sampling, bagging and random feature selection. We cover: • Random Forest intuition • Ensemble Learning • Bootstrap Sampling • Bagging • Random Feature Selection • Majority Voting • Classification vs Regression • Bias and Variance • Feature Importance • Random Forest Hyperparameters • Model Training and Evaluation • Python implementation with scikit-learn We also explore important hyperparameters including n_estimators, max_depth, max_features, min_samples_split and min_samples_leaf, and discuss how they affect model complexity and generalization. The practical section demonstrates how to train a RandomForestClassifier, generate predictions and probabilities, evaluate performance and interpret feature importance. This class is useful for Data Science students, Machine Learning learners, actuarial students, analysts and professionals who want to understand not only how to run a Random Forest model, but why it works. Subscribe to The Infinite Actuary for practical classes in Data Science, Machine Learning, Actuarial Science, Analytics and Risk. Learn the concept. Build the skill. Apply it. #RandomForest #EnsembleLearning #MachineLearning #Python #TheInfiniteActuary