Machine Learning Algorithm Recipes in scikit-learn - MachineLearningMastery.com
scikit-learnpythonsupervised-learningclassificationrecipes
Abstraction: Five standalone scikit-learn supervised classification code recipes
Key points:
- Five algorithms covered: Logistic Regression, Gaussian Naive Bayes, k-Nearest Neighbor, CART (DecisionTreeClassifier), SVM (SVC)
- All use the iris dataset from
sklearn.datasets; identical pattern: load, fit, predict, evaluate with classification_report and confusion_matrix - Multi-class logistic regression uses one-vs-all scheme automatically
- Each recipe is under 20 lines and self-contained — intended for copy-paste experimentation
- CART (Classification and Regression Trees) implemented via
DecisionTreeClassifier - Written by Jason Brownlee; companion to his Machine Learning Mastery With Python book
Connections: Scikit Learn · Supervised Learning · Classification · Machine Learning
Source: http://machinelearningmastery.com/get-your-hands-dirty-with-scikit-learn-now/