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8 articles tagged #Classification.
P.01Discover the key advantages of Random Forest algorithms — high accuracy, resistance to overfitting, feature importance, and handling missing data.
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.06Understand the KNN algorithm — how it works, distance metrics, choosing K, and its applications in both classification and regression tasks.
P.07Compare Naive Bayes, SVM, Decision Tree, and Random Forest for email spam detection with a complete Python pipeline from data loading to evaluation.
P.08Master MAE, MSE, R², RMSE, accuracy, precision, recall, F1-score, AUC-ROC, and confusion matrices with formulas and Python code examples.