Practical 10: Model Comparison & Selection

Objective

Compare multiple algorithms and select the best model for a given problem.

Duration

4-5 hours

Prerequisites

Practicals 1-9 completed

What You’ll Learn

✅ Compare multiple algorithms
✅ Use cross-validation properly
✅ Create comparison metrics
✅ Visualize performance comparisons
✅ Make informed model selection

📋 Tasks

1. Compare Multiple Models

from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.svm import SVC

models = {
    'Logistic Regression': LogisticRegression(),
    'Decision Tree': DecisionTreeClassifier(),
    'Random Forest': RandomForestClassifier(),
    'SVM': SVC()
}

results = {}
for name, model in models.items():
    model.fit(X_train, y_train)
    score = model.score(X_test, y_test)
    results[name] = score
    print(f"{name}: {score:.4f}")

2. Cross-Validation

from sklearn.model_selection import cross_val_score

for name, model in models.items():
    scores = cross_val_score(model, X, y, cv=5)
    print(f"{name}: {scores.mean():.4f} (+/- {scores.std():.4f})")

📊 Learning Outcomes

Compare multiple algorithms fairly
Use proper cross-validation
Create comparison visualizations
Make data-driven model selection
Document comparison results

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