Interactive SkLearn Series - Dimensionality Reduction
Simplify complex data. We’ll use PCA to project high-dimensional data into fewer components, reducing noise and speeding up training while preserving variance.
Interactive SkLearn Series - Clustering
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.
Interactive SkLearn Series - Voting and Stacking
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.
Interactive SkLearn Series - Boosting
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.
Interactive SkLearn Series - Bagging
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.
Interactive SkLearn Series - Evaluation Metrics (Classification)
Accuracy isn't everything. We’ll dive into Precision, Recall, and F1-Score for imbalanced datasets, and use ROC-AUC and Confusion Matrices to fully diagnose classifier performance.
Interactive SkLearn Series - Nearest Neighbors
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.
Interactive SkLearn Series - Tree-Based Models
Mimic human decision-making. We’ll visualize tree structures, understand splitting criteria like Gini impurity, and learn how to prune trees to prevent overfitting.
Interactive SkLearn Series - Support Vector Machines
Master the hyperplane. We’ll explore how SVMs use kernels (Linear, RBF, Poly) to project data into higher dimensions, allowing for precise separation of complex, non-linear classes.
Interactive SkLearn Series - Linear Classifiers
Classify data with speed. We’ll compare Logistic Regression for probability estimation against the SGDClassifier, which efficiently handles massive datasets using gradient descent.
