Python : Multiple Linear Regression Coefficients

Question

I have applied multiple linear regression to a dataset and now I want to plot the regression coefficients vs root mean squared error in a single plot using Python.

Could someone provide a code to implement this plot?

Thank you very much!

Answer 1

You can calculate RMSE as such:

from sklearn.metrics import mean_squared_error

y_true = [3, -0.5, 2, 7]

y_pred = [2.5, 0.0, 2, 8]

mse = mean_squared_error(y_true, y_pred)

rmse = mse**0.5

You can build a regression model:

import numpy as np

from sklearn.linear_model import LinearRegression

X = np.array([[1, 1], [1, 2], [2, 2], [2, 3]])

# y = 1 * x_0 + 2 * x_1 + 3

y = np.dot(X, np.array([1, 2])) + 3

reg = LinearRegression().fit(X, y)

reg.score(X, y)

reg.coef_

Then, you can plot it against any variable you wish. Although, I am not sure why you want to plot RMSE against coefficients.

import matplotlib.pyplot as plt

plt.scatter(regr.coef_, rmse)