Plot adaptive error plots and coefficient plots
In data analysis and machine learning, error plots and coefficient plots are commonly used visualization tools to evaluate the performance of a model and understand the importance of features. In this article, I will show you how to use Python to draw adaptive error plots and coefficient plots, and provide the corresponding source code.
First, we need some data to train the model. Suppose we have a regression problem and have extracted the feature matrix X and target vector y from the data set. Next, we can fit the data using a linear regression model and calculate the error between the predicted and true values.
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error
import numpy as np
# 创建线性回归模型
model = LinearRegression()
# 拟合数据
model.