G2PythonAdvancedAbout 180 min to complete
Classification
Classification is the task of predicting a category: which species, spam or not, will churn or stay. You met one classifier already — logistic regression (Notebook 46).
Notebook
About 180 minutes to complete
Enrol to read the notebook and download it to run.
What this module covers
Classification is the task of predicting a category: which species, spam or not, will churn or stay. You met one classifier already — logistic regression (Notebook 46).
By the end of it
- Use the uniform scikit-learn classifier API — fit / predict / predict_proba
- Train and contrast the core classifiers: Logistic Regression, k-Nearest Neighbors, Decision Tree, Random Forest (plus SVM and Naive Bayes)
- Evaluate with a confusion matrix, classification_report (precision/recall/F1), and ROC/AUC
- Use cross-validation for a reliable performance estimate, and diagnose overfitting vs underfitting
- Visualise decision boundaries to build intuition for each algorithm
- Read feature importances and handle multiclass problems and class imbalance