Evaluation Model Performance
Metrics scikit-learn

Model Evaluation Metrics

Learn the most important metrics to evaluate classification and regression models, with simple Python examples.

Classification Metrics

  • Accuracy: fraction of correct predictions.
  • Precision: of predicted positives, how many are correct.
  • Recall: of actual positives, how many are found.
  • F1-score: harmonic mean of precision and recall.
Confusion Matrix & Metrics
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import (
    accuracy_score,
    precision_score,
    recall_score,
    f1_score,
    confusion_matrix,
    classification_report
)

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

X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.3, random_state=42, stratify=y
)

clf = LogisticRegression(max_iter=1000)
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)

print("Accuracy:", accuracy_score(y_test, y_pred))
print("Macro Precision:", precision_score(y_test, y_pred, average="macro"))
print("Macro Recall:", recall_score(y_test, y_pred, average="macro"))
print("Macro F1:", f1_score(y_test, y_pred, average="macro"))

print("\nConfusion matrix:\n", confusion_matrix(y_test, y_pred))
print("\nClassification report:\n", classification_report(y_test, y_pred, target_names=iris.target_names))

Regression Metrics

  • MAE: Mean Absolute Error.
  • MSE: Mean Squared Error.
  • RMSE: Root Mean Squared Error.
  • R²: coefficient of determination (1 is best).
Regression Metrics Example
from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
import numpy as np

X = np.array([[500], [750], [1000], [1250], [1500], [1750], [2000]])
y = np.array([100, 150, 200, 250, 300, 320, 350])

X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.3, random_state=42
)

reg = LinearRegression()
reg.fit(X_train, y_train)
y_pred = reg.predict(X_test)

mae = mean_absolute_error(y_test, y_pred)
mse = mean_squared_error(y_test, y_pred)
rmse = np.sqrt(mse)
r2 = r2_score(y_test, y_pred)

print("MAE:", mae)
print("MSE:", mse)
print("RMSE:", rmse)
print("R²:", r2)
Next: Cross-Validation