Classification Modeling

SKILL.md

Classification Modeling

Overview

Classification modeling predicts categorical target values, assigning observations to discrete classes or categories based on input features.

When to Use

Predicting binary outcomes like customer churn, loan default, or email spam

Classifying items into multiple categories such as product types or sentiment

Building credit scoring models or risk assessment systems

Identifying disease diagnosis or medical condition from patient data

Predicting customer purchase likelihood or response to marketing

Detecting fraud, anomalies, or quality defects in production systems

Classification Types

Binary Classification: Two classes (yes/no, success/failure)

Multiclass: More than two classes

Multi-label: Multiple classes per observation

Common Algorithms

Logistic Regression: Linear classification

Decision Trees: Rule-based non-linear

Random Forest: Ensemble of decision trees

Gradient Boosting: Sequential tree building

SVM: Support Vector Machines

Naive Bayes: Probabilistic classifier

Key Metrics

Accuracy: Overall correct predictions

Precision: True positives / (true + false positives)

Recall: True positives / (true + false negatives)

F1-Score: Harmonic mean of precision/recall

AUC-ROC: Area under receiver operating characteristic curve

Implementation with Python

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split, cross_val_score
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
from sklearn.metrics import (
    confusion_matrix, classification_report, roc_auc_score, roc_curve,
    precision_recall_curve, f1_score, accuracy_score
)
import seaborn as sns

# Generate sample binary classification data
np.random.seed(42)
from sklearn.datasets import make_classification

X, y = make_classification(
    n_samples=1000, n_features=20, n_informative=10,
    n_redundant=5, random_state=42
)

X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=42
)

# Standardize features
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)

# Logistic Regression
lr_model = LogisticRegression(max_iter=1000)
lr_model.fit(X_train_scaled, y_train)
y_pred_lr = lr_model.predict(X_test_scaled)
y_proba_lr = lr_model.predict_proba(X_test_scaled)[:, 1]

print("Logistic Regression:")
print(classification_report(y_test, y_pred_lr))
print(f"AUC-ROC: {roc_auc_score(y_test, y_proba_lr):.4f}\n")

# Decision Tree
dt_model = DecisionTreeClassifier(max_depth=10, random_state=42)
dt_model.fit(X_train, y_train)
y_pred_dt = dt_model.predict(X_test)
y_proba_dt = dt_model.predict_proba(X_test)[:, 1]

print("Decision Tree:")
print(classification_report(y_test, y_pred_dt))
print(f"AUC-ROC: {roc_auc_score(y_test, y_proba_dt):.4f}\n")

# Random Forest
rf_model = RandomForestClassifier(n_estimators=100, max_depth=10, random_state=42)
rf_model.fit(X_train, y_train)
y_pred_rf = rf_model.predict(X_test)
y_proba_rf = rf_model.predict_proba(X_test)[:, 1]

print("Random Forest:")
print(classification_report(y_test, y_pred_rf))
print(f"AUC-ROC: {roc_auc_score(y_test, y_proba_rf):.4f}\n")

# Gradient Boosting
gb_model = GradientBoostingClassifier(n_estimators=100, max_depth=5, random_state=42)
gb_model.fit(X_train, y_train)
y_pred_gb = gb_model.predict(X_test)
y_proba_gb = gb_model.predict_proba(X_test)[:, 1]

print("Gradient Boosting:")
print(classification_report(y_test, y_pred_gb))
print(f"AUC-ROC: {roc_auc_score(y_test, y_proba_gb):.4f}\n")

# Confusion matrices
fig, axes = plt.subplots(2, 2, figsize=(12, 10))

models = [
    (y_pred_lr, 'Logistic Regression'),
    (y_pred_dt, 'Decision Tree'),
    (y_pred_rf, 'Random Forest'),
    (y_pred_gb, 'Gradient Boosting'),
]

for idx, (y_pred, title) in enumerate(models):
    cm = confusion_matrix(y_test, y_pred)
    ax = axes[idx // 2, idx % 2]
    sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', ax=ax)
    ax.set_title(title)
    ax.set_ylabel('True Label')
    ax.set_xlabel('Predicted Label')

plt.tight_layout()
plt.show()

# ROC Curves
plt.figure(figsize=(10, 8))

probas = [
    (y_proba_lr, 'Logistic Regression'),
    (y_proba_dt, 'Decision Tree'),
    (y_proba_rf, 'Random Forest'),
    (y_proba_gb, 'Gradient Boosting'),
]

for y_proba, label in probas:
    fpr, tpr, _ = roc_curve(y_test, y_proba)
    auc = roc_auc_score(y_test, y_proba)
    plt.plot(fpr, tpr, label=f'{label} (AUC={auc:.4f})')

plt.plot([0, 1], [0, 1], 'k--', label='Random Classifier')
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('ROC Curves Comparison')
plt.legend()
plt.grid(True, alpha=0.3)
plt.show()

# Precision-Recall Curves
plt.figure(figsize=(10, 8))

for y_proba, label in probas:
    precision, recall, _ = precision_recall_curve(y_test, y_proba)
    f1 = f1_score(y_test, (y_proba > 0.5).astype(int))
    plt.plot(recall, precision, label=f'{label} (F1={f1:.4f})')

plt.xlabel('Recall')
plt.ylabel('Precision')
plt.title('Precision-Recall Curves')
plt.legend()
plt.grid(True, alpha=0.3)
plt.show()

# Feature importance
fig, axes = plt.subplots(1, 2, figsize=(14, 5))

# Tree-based feature importance
feature_importance_rf = pd.Series(
    rf_model.feature_importances_, index=range(X.shape[1])
).sort_values(ascending=False)

axes[0].barh(range(10), feature_importance_rf.values[:10])
axes[0].set_yticks(range(10))
axes[0].set_yticklabels([f'Feature {i}' for i in feature_importance_rf.index[:10]])
axes[0].set_title('Random Forest - Top 10 Features')
axes[0].set_xlabel('Importance')

# Logistic regression coefficients
lr_coef = pd.Series(lr_model.coef_[0], index=range(X.shape[1])).abs().sort_values(ascending=False)
axes[1].barh(range(10), lr_coef.values[:10])
axes[1].set_yticks(range(10))
axes[1].set_yticklabels([f'Feature {i}' for i in lr_coef.index[:10]])
axes[1].set_title('Logistic Regression - Top 10 Features (abs coef)')
axes[1].set_xlabel('Absolute Coefficient')

plt.tight_layout()
plt.show()

# Model comparison
results = pd.DataFrame({
    'Model': ['Logistic Regression', 'Decision Tree', 'Random Forest', 'Gradient Boosting'],
    'Accuracy': [
        accuracy_score(y_test, y_pred_lr),
        accuracy_score(y_test, y_pred_dt),
        accuracy_score(y_test, y_pred_rf),
        accuracy_score(y_test, y_pred_gb),
    ],
    'AUC-ROC': [
        roc_auc_score(y_test, y_proba_lr),
        roc_auc_score(y_test, y_proba_dt),
        roc_auc_score(y_test, y_proba_rf),
        roc_auc_score(y_test, y_proba_gb),
    ],
    'F1-Score': [
        f1_score(y_test, y_pred_lr),
        f1_score(y_test, y_pred_dt),
        f1_score(y_test, y_pred_rf),
        f1_score(y_test, y_pred_gb),
    ]
})

print("Model Comparison:")
print(results)

# Cross-validation
cv_scores = cross_val_score(
    RandomForestClassifier(n_estimators=100, random_state=42),
    X_train, y_train, cv=5, scoring='roc_auc'
)
print(f"\nCross-validation AUC scores: {cv_scores}")
print(f"Mean CV AUC: {cv_scores.mean():.4f} (+/- {cv_scores.std():.4f})")

# Probability calibration
from sklearn.calibration import calibration_curve

prob_true, prob_pred = calibration_curve(y_test, y_proba_rf, n_bins=10)

plt.figure(figsize=(8, 6))
plt.plot(prob_pred, prob_true, 'o-', label='Random Forest')
plt.plot([0, 1], [0, 1], 'k--', label='Perfect Calibration')
plt.xlabel('Mean Predicted Probability')
plt.ylabel('Fraction of Positives')
plt.title('Calibration Curve')
plt.legend()
plt.grid(True, alpha=0.3)
plt.show()

Class Imbalance Handling

Oversampling: Increase minority class samples

Undersampling: Reduce majority class samples

SMOTE: Synthetic minority oversampling

Class weights: Penalize misclassifying minority class

Threshold Selection

Default (0.5): Equal misclassification cost

Custom threshold: Based on business requirements

Optimal: Maximizing F1-score or AUC

Deliverables

Classification metrics (accuracy, precision, recall, F1)

Confusion matrices for all models

ROC and Precision-Recall curves

Feature importance analysis

Model comparison table

Recommendations for best model

Probability calibration plots