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9 articles tagged #Scikit-learn.
P.01Master hyperparameter tuning with GridSearchCV using KNN, Random Forest, and SVM models with custom scoring functions.
P.02Understand decision tree algorithms for classification and regression, their pros and cons, and build an Iris classifier with Python code.
P.03Learn SVM theory including hyperplanes and the kernel trick, then build a classifier on the breast cancer dataset using scikit-learn.
P.04Build a logistic regression model to predict diabetes outcomes using the Pima Indians dataset, covering sigmoid functions, feature scaling, and evaluation.
P.05Implement K-Nearest Neighbors classification using scikit-learn with data visualization, model training, and performance evaluation on real datasets.
P.06Compare Naive Bayes, SVM, Decision Tree, and Random Forest for email spam detection with a complete Python pipeline from data loading to evaluation.
P.07Master MAE, MSE, R², RMSE, accuracy, precision, recall, F1-score, AUC-ROC, and confusion matrices with formulas and Python code examples.
P.08Build a linear regression model from scratch using scikit-learn, with data visualization, feature selection, and model evaluation metrics.
P.09EditorPickA comprehensive guide covering 10 regression types — linear, polynomial, logistic, ridge, lasso, elastic net, and more — with Python code examples and selection criteria.