This article provides an overview of how to evaluate classification model performance in scikit-learn using metrics like accuracy, precision, recall, F1 score, and ROC AUC. It includes code examples and explanations of each metric.
This crash course is designed to provide you with a solid foundation in Scikit-learn to start building machine learning models in Python. It introduces key concepts like model evaluation and selection, discuss the major algorithms like regression and classification, and walk through the typical Scikit-learn workflow for developing predictive models.
This article provides an overview of techniques like oversampling, undersampling, and adjusting class weights that can be used in scikit-learn to handle imbalanced data and improve model metrics. It also covers best practices like stratification and SMOTE oversampling.
This tutorial demonstrates key unsupervised learning techniques in scikit-learn through code examples, covering dimensionality reduction, clustering algorithms, association rule learning, and anomaly detection. A practical guide to leveraging unsupervised learning to derive insights from unlabeled datasets.
Ensemble learning combines multiple machine learning models to improve overall predictive performance. This article provides an introduction to ensemble techniques like bagging, boosting, voting, and stacking available in scikit-learn.
