Created
July 31, 2018 01:27
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Simple regression using RMSE and R² to evaluate
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# Import necessary modules | |
from sklearn.linear_model import LinearRegression | |
from sklearn.metrics import mean_squared_error | |
from sklearn.model_selection import train_test_split | |
# Create training and test sets | |
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state=42) | |
# Create the regressor: reg_all | |
reg_all = LinearRegression() | |
# Fit the regressor to the training data | |
reg_all.fit(X_train, y_train) | |
# Predict on the test data: y_pred | |
y_pred = reg_all.predict(X_test) | |
# Compute and print R^2 and RMSE | |
print("R^2: {}".format(reg_all.score(X_test, y_test))) | |
rmse = np.sqrt(mean_squared_error(y_test, y_pred)) | |
print("Root Mean Squared Error: {}".format(rmse)) |
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