statistical-learning

## What I do - Apply statistical methods to learning problems - Build regression and classification models - Perform hypothesis testing and inference - Estimate model parameters - Evaluate model uncertainty - Design experiments and A/B tests ## When to use me Use me when: - Building predictive models with uncertainty - Interpreting model decisions - Designing experiments - Making data-driven decisions - Understanding model confidence ## Key Concepts ### Regression Methods ```python import numpy as np from sklearn.linear_model import LinearRegression, Ridge, Lasso from sklearn.preprocessing import PolynomialFeatures from sklearn.model_selection import cross_val_score # Simple Linear Regression model = LinearRegression() model.fit(X, y) y_pred = model.predict(X) # Regularized Regression ridge = Ridge(alpha=1.0) # L2 penalty lasso = Lasso(alpha=1.0) # L1 penalty # Polynomial Features poly = PolynomialFeatures(degree=2, include_bias=False) X_poly = poly.fit_transform(X) # Cross-validation scores = cross_val_score(model, X, y, cv=5, scoring='r2') ``` ### Classification ```python from sklearn.linear_model import LogisticRegression from sklearn.naive_bayes import GaussianNB from sklearn.discriminant_analysis import LinearDiscriminantAnalysis # Logistic Regression log_reg = LogisticRegression() log_reg.fit(X, y) # Probabilistic outputs probs = log_reg.predict_proba(X) pred = log_reg.predict(X) # Naive Bayes (generative) nb = GaussianNB() nb.fit(X, y) # LDA (discriminative) lda = LinearDiscriminantAnalysis() lda.fit(X, y) ``` ### Model Evaluation ```python from sklearn.metrics import (accuracy_score, precision_score, recall_score, f1_score, roc_auc_score, confusion_matrix) # Classification metrics accuracy = accuracy_score(y_true, y_pred) precision = precision_score(y_true, y_pred, average='weighted') recall = recall_score(y_true, y_pred, average='weighted') f1 = f1_score(y_true, y_pred, average='weighted') auc = roc_auc_score(y_true, probas) # Regression metrics from sklearn.metrics import mean_squared_error, r2_score mse = mean_squared_error(y_true, y_pred) r2 = r2_score(y_true, y_pred) ``` ### Statistical Inference - Confidence intervals - Hypothesis testing (t-test, ANOVA) - P-values and significance - Bootstrap methods - Bayesian inference