# Mapping the Ridge Trail
def Ridge_Plot(x_train, y_train):
import numpy as np
import as plt
from sklearn.linear_model import Ridge
# Ensure that y_train is a one-dimensional array
y_train = (y_train) # Automatically convert (n, 1) to (n,)
# Define the range of regularization parameters
alphas = (-4, 4, 100)
# Storage factor
coefs = []
# Traverse each regularization parameter to train the ridge regression model
for alpha in alphas:
ridge = Ridge(alpha=alpha)
(x_train, y_train)
(ridge.coef_)
# Converting coefficients to arrays
coefs = (coefs)
# Mapping the Ridge Trail
(figsize=(10, 6))
for i in range(x_train.shape[1]):
(alphas, coefs[:, i], label=f'Feature {i + 1}')
('log')
('Regularization Parameter (alpha)')
('Coefficients')
('Ridge Trace Plot')
(0, color='black', linestyle='--', linewidth=0.7)
(loc='upper right', bbox_to_anchor=(1.2, 1), ncol=1)
plt.tight_layout()
()
Ridge_Plot(x_train=X_train,y_train=y_train)