Index A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | R | S | T | U | V | W | X A activation function advanced indexing aggregation, [1] alpha regularization apply array creation AUC B bagging, [1] Bayesian optimization bias-variance tradeoff boolean filtering boolean indexing boosting broadcasting C calibration centroid class weight classification clustering coefficients common pitfalls confidence interval confusion matrix coverage cross-validation cross-validation strategies curse of dimensionality D DataFrame datetime DBSCAN decision tree, [1] dendrogram dimensionality reduction dropna dtype E early stopping ElasticNet elbow method ensemble methods eps epsilon tube evaluation metrics explained variance extrapolation F F1 score feature engineering feature importance, [1], [2] feature scaling, [1] feature selection, [1] file I/O fillna G gamma parameter Gaussian Process genfromtxt global explanation Gradient Boosting GridSearchCV, [1] groupby H hidden layers hierarchical clustering hyperparameter optimization hyperparameter tuning, [1] I iloc imbalanced classes indexing inner join J join K K-fold K-means kernel methods KFold L L1 regularization L2 regularization label encoding Lasso Lasso regression Last-Layer Prediction Rigidity learning_rate left join linear algebra linear regression LinearRegressionUQ LLPRRegressor load loadings loadtxt loc local explanation logistic regression long format M MAE masking matrix operations max max_depth mean median melt merge method chaining min min_samples MinMaxScaler missing values MLPRegressor model comparison model interpretability Monte Carlo simulation, [1] MPIW multicollinearity, [1] N n_estimators NaN ndarray network architecture neural network nlinfit nonlinear regression np.arange np.array np.linalg np.linspace np.ones np.zeros numerical computing NumPy O one-hot encoding Optuna outer join overfitting, [1], [2] P Pandas partial dependence plot PCA PDP perplexity PICP Pipeline pivot_table polynomial features, [1] precision principal component analysis pycse pycse.sklearn Python lists vs NumPy R R-squared Random Forest, [1] random number generator random numbers RandomizedSearchCV RBF kernel read_csv recall record arrays regress relu RepeatedKFold reproducibility resample reshaping data residual analysis residuals Ridge regression right join RMSE ROC curve S save scree plot seeding Series SHAP summary plot SHAP values Shapley values sharpness sigmoid function silhouette score slicing solver split-apply-combine standardized coefficients StandardScaler, [1] std structured arrays Support Vector Regression SVR symbolic regression T t-SNE tanh time series TPE train-test split transform U UMAP uncertainties package uncertainty propagation, [1] uncertainty quantification underfitting universal approximation unsupervised learning V vectorization views vs copies W waterfall plot wide format X XGBoost
