Root Mean Squared Error vs Accuracy Linear Regression

Question

I built a simple linear regression model to predict students' final grade using this dataset https://archive.ics.uci.edu/ml/datasets/Student+Performance .

While my accuracy is very good, the errors seem to be big.

I'm not sure if I'm just not understanding the meaning of the errors correctly or if I made some errors in my code. I thought for the accuracy of 92, the errors should be way smaller and closer to 0.

Here's my code:

data = pd.read_csv("/Users/.../student/student-por.csv", sep=";")

X = np.array(data.drop([predict], 1))
y = np.array(data[predict]) 

x_train, x_test, y_train, y_test = sklearn.model_selection.train_test_split(X, y, test_size = 0.1, random_state=42)

linear = linear_model.LinearRegression()

linear.fit(x_train, y_train)

linear_accuracy = round(linear.score(x_test, y_test) , 5)

linear_mean_abs_error = metrics.mean_absolute_error(y_test, linear_prediction)
linear_mean_sq_error = metrics.mean_squared_error(y_test, linear_prediction)
linear_root_mean_sq_error = np.sqrt(metrics.mean_squared_error(y_test, linear_prediction))

Did I make any errors in the code or errors do make sense in this case?

Answer 1

The accuracy metric in sklearn linear regression is the R^2 metric. It essentially tells you the percent of the variation in the dependent variable explained by the model predictors. 0.92 is a very good score, but it does not mean that your errors will be 0. I looked your work and it seems that you used all the numeric variables as your predictors and your target was G3 . The code seems fine and the results seem accurate too. In regression tasks it is really hard to get 0 errors. Please let me know if you have any questions. Cheers