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.