Machine Learning: Random Forest, Adaboost & Decision Tree

Explore how random forests combine multiple decision trees to improve accuracy and reduce overfitting, with trees split on attributes to make classification or regression predictions.

Construct a decision tree efficiently with divide and conquer, selecting attributes that reduce uncertainty using entropy and information gain, and apply recursion to grow trees.

Learn the decision tree algorithm using entropy and information gain to pick attributes and build a root node, then recursively create leaves with a golf-playing example.

Learn how to build and visualize a decision tree classifier using the Iris dataset, with simple two-line code, feature and class names, and leaf visuals.

Explore how random forests combine multiple decision trees with random feature subsets to reduce overfitting, using averaging and hyperparameters to improve accuracy.

Explore how random forests reduce overfitting and boost accuracy by aggregating decision trees, and learn feature importance through information gain, with real-life analogies like Netflix and Titanic data.

Compare decision trees and random forests: decision trees use single rules, while random forests build multiple randomized trees to boost accuracy and reduce overfitting via sampling and cross-validation.

Explore ensemble methods, including bagging with random forests and voting schemes, and boost learning with AdaBoost, training weak classifiers sequentially to form a strong classifier.

Apply AdaBoost to sequentially train classifiers, weighing errors more in regions where earlier models faltered and combining them with a weighted average to boost accuracy.

Explores implementing the AdaBoost classifier with multiple weak classifiers, using decision tree stumps as splits, evaluating weighted error, and combining classifiers with a trainable alpha to form a strong classifier.

Learn how AdaBoost combines weak classifiers into a strong predictor by iteratively updating sample weights, computing alphas, and forming a weighted sign of classifiers.

Armando demonstrates AdaBoost efficiency by simplifying weight updates and avoiding exponentials, highlighting easy implementation, strong generalization and feature selection, while noting potential suboptimal results and sensitivity to outliers.

This AdaBoost demo guides you through building a simple AdaBoost classifier in a notebook, generating a classification dataset, fitting the model, evaluating accuracy around 98%, and inspecting feature importance.

Learners implement AdaBoost with a decision tree base estimator, visualize decision boundaries, and tune basic parameters; the demo builds random features, fits the classifier, and evaluates plotting results.

discover how to use Jupiter notebook, create Python notebooks, switch between code and markdown cells, run code, and save checkpoints, with install options like Anaconda, pip, and Google Colab.

Explore hands-on linear regression in Python using a Jupyter notebook to fit data, plot results, and compare underfitting, overfitting, and model scores.