What are the intercepts and coefficients in a linear model, and how can they be obtained?

Python
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#1

Hello, I’m new to machine learning and seeking guidance on intercepts and coefficients in linear regression. I encountered this concept in the equation:

                       y = b0 + b1 * x1 + b2 * x2 + ... + bn * xn

Could someone explain this equation to me and guide me on obtaining intercepts and coefficients for a model trained on a dataset? Example codes in Python would be appreciated for practical learning.
Thank you.

#6

In linear regression, the intercept represents the predicted variable value when all independent variables are zero, while the coefficients are weights assigned to each independent variable. To display these values, you can use Scikit-Learn.

Here’s a quick explanation of the code.

  • Import necessary modules.
  • Load the Iris dataset using load_iris().
  • Separate features (X) and target values (y).
  • Create a LinearRegression object.
  • Fit the model on the dataset with fit().
  • Assign the fitted model to model.
  • Print the intercept using intercept_.
  • Print coefficients using coef_.

This concise process leverages Scikit-Learn for linear regression analysis on the Iris dataset and I hope it proves helpful.