Classification Models

Introduction

Classification algorithms for predicting categorical outcomes.

Logistic Regression

from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, classification_report

model = LogisticRegression()
model.fit(X_train, y_train)
y_pred = model.predict(X_test)

print(f"Accuracy: {accuracy_score(y_test, y_pred)}")
print(classification_report(y_test, y_pred))

Decision Trees

from sklearn.tree import DecisionTreeClassifier

model = DecisionTreeClassifier(max_depth=5, min_samples_split=10)
model.fit(X_train, y_train)

Random Forest

from sklearn.ensemble import RandomForestClassifier

model = RandomForestClassifier(n_estimators=100, max_depth=10)
model.fit(X_train, y_train)
model.feature_importances_  # Feature importance

Support Vector Machine

from sklearn.svm import SVC

model = SVC(kernel="rbf", C=1.0, gamma="scale")
model.fit(X_train, y_train)

Practice Problems

1. Train multiple classifiers
2. Compare decision boundaries
3. Tune hyperparameters
4. Handle class imbalance
5. Visualize feature importance