statistical-learning

name	statistical-learning
description	Statistical learning methods
license	MIT
compatibility	opencode
metadata	{"audience":"machine-learning-engineers","category":"artificial-intelligence"}

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

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

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

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