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