Improve the scikit-learn skill

docs/scientific-packages.md

@@ -47,7 +47,7 @@
 - **PyMC** - Bayesian statistical modeling and probabilistic programming
 - **PyMOO** - Multi-objective optimization with evolutionary algorithms
 - **PyTorch Lightning** - Deep learning framework that organizes PyTorch code to eliminate boilerplate while maintaining full flexibility. Automates training workflows (40+ tasks including epoch/batch iteration, optimizer steps, gradient management, checkpointing), supports multi-GPU/TPU training with DDP/FSDP/DeepSpeed strategies, includes LightningModule for model organization, Trainer for automation, LightningDataModule for data pipelines, callbacks for extensibility, and integrations with TensorBoard, Wandb, MLflow for experiment tracking
-- **scikit-learn** - Machine learning algorithms, preprocessing, and model selection
+- **scikit-learn** - Industry-standard Python library for classical machine learning providing comprehensive supervised learning (classification: Logistic Regression, SVM, Decision Trees, Random Forests with 17+ variants, Gradient Boosting with XGBoost-compatible HistGradientBoosting, Naive Bayes, KNN, Neural Networks/MLP; regression: Linear, Ridge, Lasso, ElasticNet, SVR, ensemble methods), unsupervised learning (clustering: K-Means, DBSCAN, HDBSCAN, OPTICS, Agglomerative/Hierarchical, Spectral, Gaussian Mixture Models, BIRCH, MeanShift; dimensionality reduction: PCA, Kernel PCA, t-SNE, Isomap, LLE, NMF, TruncatedSVD, FastICA, LDA; outlier detection: IsolationForest, LocalOutlierFactor, OneClassSVM), data preprocessing (scaling: StandardScaler, MinMaxScaler, RobustScaler; encoding: OneHotEncoder, OrdinalEncoder, LabelEncoder; imputation: SimpleImputer, KNNImputer, IterativeImputer; feature engineering: PolynomialFeatures, KBinsDiscretizer, text vectorization with CountVectorizer/TfidfVectorizer), model evaluation (cross-validation: KFold, StratifiedKFold, TimeSeriesSplit, GroupKFold; hyperparameter tuning: GridSearchCV, RandomizedSearchCV, HalvingGridSearchCV; metrics: 30+ evaluation metrics for classification/regression/clustering including accuracy, precision, recall, F1, ROC-AUC, MSE, R², silhouette score), and Pipeline/ColumnTransformer for production-ready workflows. Features consistent API (fit/predict/transform), extensive documentation, integration with NumPy/pandas/SciPy, joblib persistence, and scikit-learn-compatible ecosystem (XGBoost, LightGBM, CatBoost, imbalanced-learn). Optimized implementations using Cython/OpenMP for performance. Use cases: predictive modeling, customer segmentation, anomaly detection, feature engineering, model selection/validation, text classification, image classification (with feature extraction), time series forecasting (with preprocessing), medical diagnosis, fraud detection, recommendation systems, and any tabular data ML task requiring interpretable models or established algorithms
 - **scikit-survival** - Survival analysis and time-to-event modeling with censored data. Built on scikit-learn, provides Cox proportional hazards models (CoxPHSurvivalAnalysis, CoxnetSurvivalAnalysis with elastic net regularization), ensemble methods (Random Survival Forests, Gradient Boosting), Survival Support Vector Machines (linear and kernel), non-parametric estimators (Kaplan-Meier, Nelson-Aalen), competing risks analysis, and specialized evaluation metrics (concordance index, time-dependent AUC, Brier score). Handles right-censored data, integrates with scikit-learn pipelines, and supports feature selection and hyperparameter tuning via cross-validation
 - **SHAP** - Model interpretability and explainability using Shapley values from game theory. Provides unified approach to explain any ML model with TreeExplainer (fast exact explanations for XGBoost/LightGBM/Random Forest), DeepExplainer (TensorFlow/PyTorch neural networks), KernelExplainer (model-agnostic), and LinearExplainer. Includes comprehensive visualizations (waterfall plots for individual predictions, beeswarm plots for global importance, scatter plots for feature relationships, bar/force/heatmap plots), supports model debugging, fairness analysis, feature engineering guidance, and production deployment
 - **Stable Baselines3** - PyTorch-based reinforcement learning library providing reliable implementations of RL algorithms (PPO, SAC, DQN, TD3, DDPG, A2C, HER, RecurrentPPO). Use this skill for training RL agents on standard or custom Gymnasium environments, implementing callbacks for monitoring and control, using vectorized environments for parallel training, creating custom environments with proper Gymnasium API implementation, and integrating with deep RL workflows. Includes comprehensive training templates, evaluation utilities, algorithm selection guidance (on-policy vs off-policy, continuous vs discrete actions), support for multi-input policies (dict observations), goal-conditioned learning with HER, and integration with TensorBoard for experiment tracking