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)
• Reinforcement Learning: Learn through interaction with environment
  • Policy-based: Learn policy directly (REINFORCE, PPO)
  • Value-based: Learn value function (DQN, SARSA)

Algorithm Selection Criteria

• Data Size: Small vs. large datasets
• Feature Types: Numerical, categorical, text, image