classification-modeling

name	Classification Modeling
description	Build binary and multiclass classification models using logistic regression, decision trees, and ensemble methods for categorical prediction and classification

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()


dt_model = DecisionTreeClassifier(max_depth=, random_state=)
dt_model.fit(X_train, y_train)
y_pred_dt = dt_model.predict(X_test)
y_proba_dt = dt_model.predict_proba(X_test)[:, ]

()
(classification_report(y_test, y_pred_dt))
()


rf_model = RandomForestClassifier(n_estimators=, max_depth=, random_state=)
rf_model.fit(X_train, y_train)
y_pred_rf = rf_model.predict(X_test)
y_proba_rf = rf_model.predict_proba(X_test)[:, ]

()
(classification_report(y_test, y_pred_rf))
()


gb_model = GradientBoostingClassifier(n_estimators=, max_depth=, random_state=)
gb_model.fit(X_train, y_train)
y_pred_gb = gb_model.predict(X_test)
y_proba_gb = gb_model.predict_proba(X_test)[:, ]

()
(classification_report(y_test, y_pred_gb))
()


fig, axes = plt.subplots(, , figsize=(, ))

models = [
    (y_pred_lr, ),
    (y_pred_dt, ),
    (y_pred_rf, ),
    (y_pred_gb, ),
]

 idx, (y_pred, title)  (models):
    cm = confusion_matrix(y_test, y_pred)
    ax = axes[idx // , idx % ]
    sns.heatmap(cm, annot=, fmt=, cmap=, ax=ax)
    ax.set_title(title)
    ax.set_ylabel()
    ax.set_xlabel()

plt.tight_layout()
plt.show()


plt.figure(figsize=(, ))

probas = [
    (y_proba_lr, ),
    (y_proba_dt, ),
    (y_proba_rf, ),
    (y_proba_gb, ),
]

 y_proba, label  probas:
    fpr, tpr, _ = roc_curve(y_test, y_proba)
    auc = roc_auc_score(y_test, y_proba)
    plt.plot(fpr, tpr, label=)

plt.plot([, ], [, ], , label=)
plt.xlabel()
plt.ylabel()
plt.title()
plt.legend()
plt.grid(, alpha=)
plt.show()


plt.figure(figsize=(, ))

 y_proba, label  probas:
    precision, recall, _ = precision_recall_curve(y_test, y_proba)
    f1 = f1_score(y_test, (y_proba > ).astype())
    plt.plot(recall, precision, label=)

plt.xlabel()
plt.ylabel()
plt.title()
plt.legend()
plt.grid(, alpha=)
plt.show()


fig, axes = plt.subplots(, , figsize=(, ))


feature_importance_rf = pd.Series(
    rf_model.feature_importances_, index=(X.shape[])
).sort_values(ascending=)

axes[].barh((), feature_importance_rf.values[:])
axes[].set_yticks(())
axes[].set_yticklabels([  i  feature_importance_rf.index[:]])
axes[].set_title()
axes[].set_xlabel()


lr_coef = pd.Series(lr_model.coef_[], index=(X.shape[])).().sort_values(ascending=)
axes[].barh((), lr_coef.values[:])
axes[].set_yticks(())
axes[].set_yticklabels([  i  lr_coef.index[:]])
axes[].set_title()
axes[].set_xlabel()

plt.tight_layout()
plt.show()


results = pd.DataFrame({
    : [, , , ],
    : [
        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),
    ],
    : [
        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(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),
    ]
})

()
(results)


cv_scores = cross_val_score(
    RandomForestClassifier(n_estimators=, random_state=),
    X_train, y_train, cv=, scoring=
)
()
()


 sklearn.calibration  calibration_curve

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

plt.figure(figsize=(, ))
plt.plot(prob_pred, prob_true, , label=)
plt.plot([, ], [, ], , label=)
plt.xlabel()
plt.ylabel()
plt.title()
plt.legend()
plt.grid(, alpha=)
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