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
• Interpretability: Need for model explanations
• Training Time: Constraints on model training
• Inference Latency: Real-time vs. batch predictions
• Accuracy Requirements: Trade-offs with complexity

Common ML Frameworks

• scikit-learn: Traditional ML algorithms, easy to use
• TensorFlow/Keras: Deep learning, production-ready
• PyTorch: Research-friendly, dynamic computation graphs
• XGBoost/LightGBM: Gradient boosting for tabular data
• Hugging Face Transformers: Pre-trained NLP models

Feature Engineering Techniques

Numerical Features

• Scaling: Standardization (z-score) or Min-Max scaling
• Binning: Convert continuous to categorical
• Polynomial Features: Create interaction terms
• Log Transformations: Handle skewed distributions
• Normalization: Scale to unit norm

Categorical Features

• One-Hot Encoding: Binary columns for each category
• Label Encoding: Map categories to integers
• Ordinal Encoding: Preserve order for ordinal categories
• Target Encoding: Replace with target mean (with regularization)
• Embedding: Learn dense representations (for high cardinality)

Text Features

• Bag of Words: Word frequency counts
• TF-IDF: Term frequency-inverse document frequency
• N-grams: Capture word sequences
• Word Embeddings: Pre-trained (Word2Vec, GloVe) or learned
• Transformer Embeddings: Contextual embeddings (BERT, RoBERTa)

Feature Selection

• Filter Methods: Statistical tests, correlation analysis
• Wrapper Methods: Recursive feature elimination, forward/backward selection
• Embedded Methods: L1 regularization, tree-based feature importance
• Dimensionality Reduction: PCA, LDA, autoencoders

Hyperparameter Tuning Strategies

Search Strategies

• Grid Search: Exhaustive search over parameter grid
• Random Search: Random sampling from parameter space
• Bayesian Optimization: Use probabilistic model to guide search
• Evolutionary Algorithms: Genetic algorithms for parameter evolution
• Successive Halving: Early stopping for poor configurations

Common Hyperparameters

• Tree-based Models: max_depth, n_estimators, learning_rate, min_samples_split
• Neural Networks: learning_rate, batch_size, number of layers, number of units
• SVM: C, kernel, gamma
• K-Means: n_clusters, init, n_init

Tuning Best Practices

• Cross-Validation: Use k-fold or stratified k-fold for robust evaluation
• Early Stopping: Stop training when validation performance degrades
• Learning Rate Schedules: Decay learning rate over time
• Ensembling: Combine multiple models for better performance

Evaluation Metrics and Validation Methods

Regression Metrics

• Mean Squared Error (MSE): Average of squared errors
• Root Mean Squared Error (RMSE): Square root of MSE
• Mean Absolute Error (MAE): Average of absolute errors
• R-squared: Proportion of variance explained
• Mean Absolute Percentage Error (MAPE): Percentage-based error

Classification Metrics

• Accuracy: Overall correct predictions
• Precision: True positives / (true positives + false positives)
• Recall: True positives / (true positives + false negatives)
• F1-Score: Harmonic mean of precision and recall
• ROC-AUC: Area under ROC curve
• Confusion Matrix: Detailed breakdown of predictions

Validation Methods

• Train-Test Split: Simple holdout validation
• K-Fold Cross-Validation: Divide data into k folds
• Stratified K-Fold: Preserve class distribution in folds
• Time Series Split: Respect temporal order
• Nested Cross-Validation: Outer loop for evaluation, inner for tuning

Bias-Variance Trade-off

• High Bias: Underfitting, model too simple
• High Variance: Overfitting, model too complex
• Sweet Spot: Balance between bias and variance
• Regularization: Reduce variance by adding constraints

Model Interpretation

Feature Importance

• Permutation Importance: Shuffle feature values and measure impact
• SHAP Values: Game-theoretic approach to feature attribution
• LIME: Local interpretable model-agnostic explanations
• Partial Dependence Plots: Show relationship between feature and predictions

Model-Agnostic Methods

• SHAP: Consistent, local feature attribution
• LIME: Local linear approximations
• Permutation Importance: Global feature importance
• Partial Dependence: Global relationship visualization

Model-Specific Methods

• Linear Models: Coefficients directly show feature impact
• Tree-based Models: Feature importance from split criteria
• Neural Networks: Attention weights, saliency maps