a65614aa1f06b36792342549569975740c0df0952a29b745b249465a3f56c1


from sklearn.metrics import mean_squared_error
import numpy as np
import matplotlib.pyplot as plt

from sklearn.linear_model import LinearRegression

linear = LinearRegression()
linear.fit(X_train_scaled, y_train)

y_train_pred = model.predict(X_train_scaled)
y_test_pred = model.predict(X_test_scaled)

train_score = model.score(X_train_scaled, y_train)
print("Training R2 score = {:.3f}".format(train_score))

train_mse = mean_squared_error(y_train, y_train_pred)
print('Training RMSE = {:.3f} MPA'.format(np.sqrt(train_mse)))


test_score = model.score(X_test_scaled, y_test)
print("Test R2 score = {:.3f}".format(test_score))

# RMSE
test_mse = mean_squared_error(y_test, y_test_pred)
print('Test RMSE = {:.3f} MPA'.format(np.sqrt(test_mse)))


fig, ax = plt.subplots(1,2, figsize=(8,4))
fig.suptitle('Linear model', fontsize=16)

ax[0].scatter(y_train, y_train_pred)
ax[0].axline((0, 0), slope=1, c='black')

ax[1].scatter(y_test, y_test_pred)
ax[1].axline((0, 0), slope=1, c='black')

plt.show() 이런거 보신적 있는분 보통 어떨때 나오는 현상이에요?