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.