Scikit-learn Articles

This article is a comprehensive guide to Scikit-learn Pipelines, showing how to streamline machine learning workflows by combining preprocessing and modeling into one efficient, reusable process. You'll learn how to handle missing values with SimpleImputer, encode categorical features using OneHotEncoder, scale numerical data with StandardScaler, and organize these transformations with a ColumnTransformer. Beyond preprocessing, the article demonstrates building modular models with Pipeline, tuning hyperparameters using GridSearchCV, and combining models with a VotingClassifier for ensemble learning. Whether you're new to pipelines or looking to implement advanced, production-ready workflows, this guide provides practical, step-by-step examples to help you build clean, scalable machine learning systems.

This article explains ensemble methods in scikit-learn, covering Random Forest, Boosting, Stacking, and VotingClassifier techniques with practical examples for both classification and regression. It includes hands-on demonstrations of Random Forest Classifier and Regressor, AdaBoost and Gradient Boosting models, and Stacking Classifier and Regressor, helping readers understand how ensemble learning improves accuracy and model performance. This guide is ideal for anyone looking to learn, compare, and apply scikit-learn ensemble algorithms to real-world machine learning problems.

This article provides a practical overview of regularization and hyperparameter tuning in machine learning, focusing on how these techniques improve model performance and prevent overfitting. It explains common regularization methods and demonstrates systematic hyperparameter optimization using GridSearchCV. Through a clear, hands-on example with Support Vector Classifier (SVC), the article shows how tuning parameters such as C and kernel settings leads to better generalization and more reliable predictive models.