ML Best Practices Model Selection Guidelines Problem Type Classification - Supervised Learning : Labeled data for training - Regression: Predict continuous values (Linear Regression, Random Forest, Gradient Boosting) - Classification: Predict discrete labels (Logistic Regression, SVM, Decision Trees, Neural Networks) - Unsupervised Learning : Unlabeled data exploration - Clustering: Group similar data points (K-Means, DBSCAN, Hierarchical) - Dimensionality Reduction: Reduce feature space (PCA, t-SNE, UMAP) - Anomaly Detection: Identify outliers (Isolation Forest, One-Class SVM) - Reinforcemen…