How do I compare different machine learning models in Python?

How do I compare different machine learning models in Python?

You can compare models using metrics like accuracy, precision, recall, and F1 score. Libraries like scikit-learn provide tools for this.

What metrics should I focus on for classification problems?

For classification, focus on accuracy, precision, recall, and the F1 score. Each metric gives different insights into model performance.

Can you show me an example of how to implement this in code?

Sure! Here's a simple example using scikit-learn:

from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression

# Load your dataset
X, y = load_your_data()
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

# Train models
rf_model = RandomForestClassifier().fit(X_train, y_train)
log_model = LogisticRegression().fit(X_train, y_train)

# Make predictions
rf_predictions = rf_model.predict(X_test)
log_predictions = log_model.predict(X_test)

# Compare models
print(classification_report(y_test, rf_predictions))
print(classification_report(y_test, log_predictions))

What if I want to visualize the comparison?

You can use libraries like Matplotlib or Seaborn to visualize model performance. For example, you can plot ROC curves or bar charts of accuracy.