这是岭回归的岭迹图
import pandas as pd
import numpy as np
from sklearn import model_selection
from sklearn.linear_model import Ridge
import matplotlib.pyplot as plt
data = pd.read_csv(r'')
# 拆分为训练集和测试集
predictors = data.columns[2:]
x_train, x_test, y_train, y_test = model_selection.train_test_split(data[predictors], data.fuel,
test_size=0.25, random_state=1234)
# 构造不同的lambda值
Lambdas = np.logspace(-5, 2, 200)
# 存放偏回归系数
ridge_cofficients = []
for Lambda in Lambdas:
ridge = Ridge(alpha=Lambda, normalize=True)
ridge.fit(x_train, y_train)
ridge_cofficients.append(ridge.coef_)
# 绘制岭迹曲线
plt.rcParams['font.sans-serif'] = ['Microsoft YaHei']
plt.rcParams['axes.unicode_minus'] = False
plt.style.use('ggplot')
plt.plot(Lambdas, ridge_cofficients)
# x轴做对数处理
plt.xscale('log')
plt.xlabel('λ')
plt.ylabel('Cofficients')
plt.show()
怎么修改画出lasso的λ的图
另外，怎么将图上的线条加上标号，表明是那个变量的
