interactive-sklearn-series

Identify the rare and the strange. We’ll apply Isolation Forests and One-Class SVMs to detect outliers, useful for fraud detection, network security, and cleaning training data.

Visualize the impossible. We’ll use t-SNE and UMAP to map high-dimensional datasets into 2D or 3D space, revealing local structures and clusters that linear methods like PCA miss.

Simplify complex data. We’ll use PCA to project high-dimensional data into fewer components, reducing noise and speeding up training while preserving variance.

Discover hidden groups. We’ll use K-Means for centroid-based grouping, DBSCAN to find arbitrary shapes and outliers, and Hierarchical Clustering to build taxonomies.

Two heads are better than one. We’ll use Voting Classifiers to average predictions and Stacking to train a meta-model that learns how to best combine the outputs of diverse base models.

Reduce bias by learning from mistakes. We’ll implement AdaBoost and Gradient Boosting, and specifically look at Histogram-based Gradient Boosting for state-of-the-art speed on large data.

Reduce variance by combining models. We’ll explore Random Forests and ExtraTrees, learning how they aggregate predictions from many decision trees to create a robust, stable estimator.

Predict based on proximity. We’ll use K-Nearest Neighbors (KNN) for classification, understanding how distance metrics work and why the "curse of dimensionality" affects performance.

Mimic human decision-making. We’ll visualize tree structures, understand splitting criteria like Gini impurity, and learn how to prune trees to prevent overfitting.