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Understanding Random Forest using Python (scikit-learn)
- Decision trees are a popular supervised learning algorithm, but are prone to overfitting, leading people to use ensemble models like Random Forests.
- Bagging involves creating multiple training sets from the original dataset by bootstrapping and aggregating multiple decision trees.
- Random Forests differ by randomly selecting features at each decision node, reducing overfitting and improving generalization.
- Random Forests utilize sampling with replacement for bootstrapped datasets and sampling without replacement for feature selection.
- Out-of-Bag (OOB) evaluation allows estimating generalization error by excluding some training data from each tree.
- Training a Random Forest includes creating a baseline model, tuning hyperparameters with Grid Search, and evaluating the final model.
- Feature importance in Random Forests can be calculated using Mean Decrease in Impurity or Permutation Importance methods.
- Visualizing individual decision trees in a Random Forest can illustrate how differently each tree splits the data.
- Random Forests remain popular for tabular data analysis due to their simplicity, interpretability, and parallelizability.
- The tutorial covers bagging, Random Forest differences, training, tuning, feature importance, and visualization using Python with scikit-learn.
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