# Core Capabilities

Supervised learning, unsupervised learning, model evaluation and selection, data
preprocessing, and pipelines and composition. Per-topic detail is in the other reference
files in this directory.

## Core Capabilities

### 1. Supervised Learning

Comprehensive algorithms for classification and regression tasks.

**Key algorithms:**
- **Linear models**: Logistic Regression, Linear Regression, Ridge, Lasso, ElasticNet
- **Tree-based**: Decision Trees, Random Forest, Gradient Boosting
- **Support Vector Machines**: SVC, SVR with various kernels
- **Ensemble methods**: AdaBoost, Voting, Stacking
- **Neural Networks**: MLPClassifier, MLPRegressor
- **Others**: Naive Bayes, K-Nearest Neighbors

**When to use:**
- Classification: Predicting discrete categories (spam detection, image classification, fraud detection)
- Regression: Predicting continuous values (price prediction, demand forecasting)

**See:** `references/supervised_learning.md` for detailed algorithm documentation, parameters, and usage examples.

### 2. Unsupervised Learning

Discover patterns in unlabeled data through clustering and dimensionality reduction.

**Clustering algorithms:**
- **Partition-based**: K-Means, MiniBatchKMeans
- **Density-based**: DBSCAN, HDBSCAN, OPTICS
- **Hierarchical**: AgglomerativeClustering
- **Probabilistic**: Gaussian Mixture Models
- **Others**: MeanShift, SpectralClustering, BIRCH

**Dimensionality reduction:**
- **Linear**: PCA, TruncatedSVD, NMF
- **Manifold learning**: t-SNE, Isomap, LLE, MDS, ClassicalMDS (1.8+)
- **External (install separately)**: UMAP (`umap-learn`)
- **Feature extraction**: FastICA, LatentDirichletAllocation

**When to use:**
- Customer segmentation, anomaly detection, data visualization
- Reducing feature dimensions, exploratory data analysis
- Topic modeling, image compression

**See:** `references/unsupervised_learning.md` for detailed documentation.

### 3. Model Evaluation and Selection

Tools for robust model evaluation, cross-validation, and hyperparameter tuning.

**Cross-validation strategies:**
- KFold, StratifiedKFold (classification)
- TimeSeriesSplit (temporal data)
- GroupKFold (grouped samples)

**Hyperparameter tuning:**
- GridSearchCV (exhaustive search)
- RandomizedSearchCV (random sampling)
- HalvingGridSearchCV (successive halving)

**Metrics:**
- **Classification**: accuracy, precision, recall, F1-score, ROC AUC, confusion matrix
- **Regression**: MSE, RMSE, MAE, R², MAPE
- **Clustering**: silhouette score, Calinski-Harabasz, Davies-Bouldin

**When to use:**
- Comparing model performance objectively
- Finding optimal hyperparameters
- Preventing overfitting through cross-validation
- Understanding model behavior with learning curves

**See:** `references/model_evaluation.md` for comprehensive metrics and tuning strategies.

### 4. Data Preprocessing

Transform raw data into formats suitable for machine learning.

**Scaling and normalization:**
- StandardScaler (zero mean, unit variance)
- MinMaxScaler (bounded range)
- RobustScaler (robust to outliers)
- Normalizer (sample-wise normalization)

**Encoding categorical variables:**
- OneHotEncoder (nominal categories)
- OrdinalEncoder (ordered categories)
- LabelEncoder (target encoding)

**Handling missing values:**
- SimpleImputer (mean, median, most frequent)
- KNNImputer (k-nearest neighbors)
- IterativeImputer (multivariate imputation)

**Feature engineering:**
- PolynomialFeatures (interaction terms)
- KBinsDiscretizer (binning)
- Feature selection (RFE, SelectKBest, SelectFromModel)

**When to use:**
- Before training any algorithm that requires scaled features (SVM, KNN, Neural Networks)
- Converting categorical variables to numeric format
- Handling missing data systematically
- Creating non-linear features for linear models

**See:** `references/preprocessing.md` for detailed preprocessing techniques.

### 5. Pipelines and Composition

Build reproducible, production-ready ML workflows.

**Key components:**
- **Pipeline**: Chain transformers and estimators sequentially
- **ColumnTransformer**: Apply different preprocessing to different columns
- **FeatureUnion**: Combine multiple transformers in parallel
- **TransformedTargetRegressor**: Transform target variable

**Benefits:**
- Prevents data leakage in cross-validation
- Simplifies code and improves maintainability
- Enables joint hyperparameter tuning
- Ensures consistency between training and prediction

**When to use:**
- Always use Pipelines for production workflows
- When mixing numerical and categorical features (use ColumnTransformer)
- When performing cross-validation with preprocessing steps
- When hyperparameter tuning includes preprocessing parameters

**See:** `references/pipelines_and_composition.md` for comprehensive pipeline patterns.