#model-selection

Master systematic model selection and optimization for machine learning projects with performance evaluation frameworks. Model selection process: 1. Problem definition: classification vs. regression, supervised vs. unsupervised learning. 2. Data assessment: sample size (minimum 1000 for deep learning), feature count, missing values analysis. 3. Baseline models: linear regression, logistic regression, random forest for initial benchmarks. Algorithm comparison: 1. Tree-based: Random Forest (high interpretability), XGBoost (competition winner), LightGBM (fast training). 2. Linear models: Ridge/Lasso (regularization), ElasticNet (feature selection), SGD (large datasets). 3. Neural networks: MLPs (tabular data), CNNs (images), RNNs/Transformers (sequences). Hyperparameter optimization: 1. Grid search: exhaustive parameter combinations, computationally expensive but thorough. 2. Random search: efficient for high-dimensional spaces, 60% less computation time. 3. Bayesian optimization: intelligent search using Gaussian processes, tools like Optuna, Hyperopt. Cross-validation strategies: 1. K-fold CV: k=5 for small datasets, k=10 for larger datasets, stratified for imbalanced data. 2. Time series CV: walk-forward validation, expanding window, respect temporal order. Performance metrics: accuracy (>85% target), precision/recall (F1 >0.8), AUC-ROC (>0.9 excellent), confusion matrix analysis for class-specific performance.