Sign in Subscribe
10 min read interactive-sklearn-series

Interactive SkLearn Series - Evaluation Metrics (Regression)

How good is your prediction? We’ll evaluate performance using standard metrics like RMSE and MAE, and learn when to use R-squared versus Median Absolute Error for different business goals.
Interactive SkLearn Series - Evaluation Metrics (Regression)
Scikit-Learn Tutorial: Regression Metrics

Tutorial Loading...

Loading NumPy...
-- / --

Introduction

main.py
Terminal Output
Initializing System...

Published by:

WassupAI

You might also like...

07
Dec
Interactive SkLearn Series - Evaluation Metrics (Classification)

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.
10 min read
07
Dec
Interactive SkLearn Series - Nearest Neighbors

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.
7 min read
07
Dec
Interactive SkLearn Series - Tree-Based Models

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.
9 min read
07
Dec
Interactive SkLearn Series - Support Vector Machines

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.
10 min read
07
Dec
Interactive SkLearn Series - Linear Classifiers

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.
9 min read

Member discussion