4. MODEL SELECTION (HIGH IMPORTANCE - Fourth Priority) (5/5)
│ ├── 4.1 Algorithm Selection (5/5)
│ │ ├── Supervised Learning Algorithms (5/5)
│ │ │ ├── Classification (e.g., Logistic Regression, SVM, Decision Trees, Random Forest, XGBoost)
│ │ │ │ ├── sklearn.linear_model
│ │ │ │ ├── sklearn.svm
│ │ │ │ ├── sklearn.tree
│ │ │ │ ├── sklearn.ensemble
│ │ │ │ ├── xgboost
│ │ │ │ └── ✓ Choosing the right algorithm based on problem type and data characteristics
│ │ │ └── Regression (e.g., Linear Regression, Ridge, Lasso, SVR, Gradient Boosting)
│ │ │ ├── sklearn.linear_model
│ │ │ ├── sklearn.svm
│ │ │ ├── sklearn.ensemble
│ │ │ └── ✓ Selecting models for continuous target variables
│ │ └── Unsupervised Learning Algorithms (4/5)
│ │ ├── Clustering (e.g., K-Means, DBSCAN, Hierarchical Clustering)
│ │ │ ├── sklearn.cluster
│ │ │ └── ✓ Grouping similar data points
│ │ └── Dimensionality Reduction (e.g., PCA, t-SNE)
│ │ ├── sklearn.decomposition
│ │ ├── sklearn.manifold
│ │ └── ✓ Reducing feature space for visualization or efficiency
│ │
│ ├── 4.2 Hyperparameter Tuning (5/5)
│ │ ├── Grid Search (5/5)
│ │ │ ├── sklearn.model_selection.GridSearchCV
│ │ │ └── ✓ Exhaustive search over a specified parameter grid
│ │ ├── Random Search (4/5)
│ │ │ ├── sklearn.model_selection.RandomizedSearchCV
│ │ │ └── ✓ Random search over parameters from a distribution
│ │ ├── Bayesian Optimization (3/5)
│ │ │ ├── scikit-optimize
│ │ │ ├── hyperopt
│ │ │ └── ✓ Uses probabilistic model to find optimal hyperparameters efficiently
│ │ └── Automated ML (AutoML) (2/5)
│ │ ├── Auto-Sklearn
│ │ ├── TPOT
│ │ └── ✓ Automates hyperparameter tuning and model selection
│ │
│ └── 4.3 Cross-Validation Strategies (5/5)
│ ├── K-Fold Cross-Validation (5/5)
│ │ ├── sklearn.model_selection.KFold
│ │ ├── sklearn.model_selection.cross_val_score
│ │ └── ✓ Standard for robust model evaluation, reduces variance
│ ├── Stratified K-Fold (5/5)
│ │ ├── sklearn.model_selection.StratifiedKFold
│ │ └── ✓ Preserves class proportions in each fold, essential for imbalanced data
│ ├── Leave-One-Out Cross-Validation (LOOCV) (2/5)
│ │ ├── sklearn.model_selection.LeaveOneOut
│ │ └── ✓ High computational cost, used for small datasets
│ └── Time Series Cross-Validation (3/5)
│ ├── sklearn.model_selection.TimeSeriesSplit
│ └── ✓ Preserves temporal order, crucial for time series models