History & Evolution: Discover how Random Forests evolved from the Classification and Regression Trees (CART) introduced in 1984, and how the Random Subspace Method was pioneered by Tin Kam Ho in 1995. We explain how Leo Breiman combined bagging (bootstrap aggregating) and random feature selection to create this powerful ensemble model in 2001.How it Works: Learn how multiple decision trees vote to make a single, reliable prediction. We explain how this method effectively reduces the high variance (overfitting) commonly seen in individual deep decision trees.Random Forest vs. Gradient Boosting: We compare Random Forests (which build trees in parallel to reduce variance) with Gradient Boosting (which builds trees sequentially to reduce bias) to help you decide which algorithm fits your project best.Feature Importance Explained: Understand the crucial difference between the default Mean Decrease in Impurity (MDI / Gini Importance) and Permutation Feature Importance. Find out why MDI can be heavily biased toward high-cardinality continuous features, and why permutation importance often provides a more reliable and robust metric on unseen data.Hyperparameter Tuning: See how to optimize your model using scikit-learn parameters like n_estimators, max_depth, min_samples_split, and max_features using Random Search methods.Extrapolation Limitations: We explore a major limitation of tree-based models—their strict inability to extrapolate outside the range of their training data—and discuss modern statistical solutions like the progression method.Real-World Applications: Explore practical use cases, from land cover classification using QGIS and Sentinel-2 satellite imagery, to high-dimensional genomic data analysis, and financial risk prediction.If you find this video helpful, please LIKE, SUBSCRIBE, and turn on NOTIFICATIONS for more machine learning tutorials! Let us know in the comments which algorithm you want us to cover next.📌 Resources & Mentions:Scikit-Learn Random Forest Classifier DocumentationQGIS Semi-Automatic Classification Plugin for Remote SensingOut-of-Bag (OOB) Error Estimation--------------------------------------------------------------------------------#Tags#RandomForest #MachineLearning #DataScience #ScikitLearn #Python #DecisionTrees #GradientBoosting #EnsembleLearning #FeatureImportance #MachineLearningAlgorithms #DataAnalysis #HyperparameterTuning #ArtificialIntelligence #RemoteSensing #DataEngineering