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Home
Python 3
Evaluating Model Performance

Evaluating Model Performance

Evaluating Model Performance

Evaluating Model Performance

Evaluate prediction quality using regression metrics.

Mean Absolute Error (MAE)

mae = mean_absolute_error(
Y_test,
predictions
)
print(mae)

Mean Squared Error (MSE)

mse = mean_squared_error(
Y_test,
predictions
)
print(mse)

Root Mean Squared Error (RMSE)

rmse = np.sqrt(mse)
print(rmse)

R² Score

r2 = r2_score(
Y_test,
predictions
)
print(r2)

Explanation

These metrics help measure:

Average prediction error
Overall prediction accuracy
Model reliability

Lower MAE, MSE, and RMSE indicate better predictions, while an R² Score closer to 1 indicates stronger model performance.

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Evaluating Model Performance

Contents

Evaluating Model Performance

Mean Absolute Error (MAE)

Mean Squared Error (MSE)

Root Mean Squared Error (RMSE)

R² Score

Explanation

Stock Price Prediction for Beginners using Data Science

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GIT Guide

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Spring Boot Tutorial

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Java Examples Tutorial

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