How do I evaluate my machine learning model using scikit-learn?

How do I evaluate my machine learning model using scikit-learn?

You can evaluate your model using various metrics like accuracy, precision, recall, and F1 score. Would you like to see examples?

Yes, please show me how to calculate accuracy and F1 score.

To calculate accuracy, you can use accuracy_score(y_true, y_pred). For F1 score, use f1_score(y_true, y_pred). Do you have a specific dataset in mind?

I'm using the Iris dataset. Can you provide a code snippet?

Sure! Here's a simple example:

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, f1_score

# Load data
iris = load_iris()
X, y = iris.data, iris.target

# Split data
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

# Train model
model = RandomForestClassifier()
model.fit(X_train, y_train)

# Make predictions
y_pred = model.predict(X_test)

# Evaluate
accuracy = accuracy_score(y_test, y_pred)
f1 = f1_score(y_test, y_pred, average='weighted')
print(f'Accuracy: {accuracy}, F1 Score: {f1}')

Thanks! How do I interpret the F1 score?

The F1 score is the harmonic mean of precision and recall. A score of 1 indicates perfect precision and recall, while a score of 0 indicates the worst performance. It's useful for imbalanced datasets.