# What else does the LinearRegression function provide?

### Question

In the context of this exercise, what else does the `LinearRegression` function provide?

There are a few other things that the `LinearRegression` function provides and lets us do.

When creating a `LinearRegression` model, you can choose to determine whether it should calculate any intercept for the model, by setting the `fit_intercept` parameter to `True` or `False`. If you choose not to calculate any intercept for the model, it will except that the data is already centered. In addition, you can also set other parameters such as `normalize`, `copy_X` and `n_jobs`.

In addition, you can obtain all the parameters of the model’s estimator using the `get_params()` method, and, you can use the `score()` method to obtain the R^2 score, which is a value telling how close the data is to the regression line.

To see a full list of all you can do with the `LinearRegression` function, with more details on each method and parameter, you can also check out the documentation.

8 Likes

For anyone who may have been puzzled by the wording regarding fit_intercept, here is the explanation from the documentation

fit_intercept : boolean, optional, default True

whether to calculate the intercept for this model. If set to False, no intercept will be used in calculations (e.g. data is expected to be already centered).

2 Likes

Just curious

The `.fit()` method gives the model two variables that are useful to us:

1. the `line_fitter.coef_` , which contains the slope
2. the `line_fitter.intercept_` , which contains the intercept

Is there any point to print these figures out?

I tried to print it but was unable to find the variables

1 Like

great question, I’d also like to know

Just print it:

``````# variable name = line_fitter
print(line_fitter.coef_)
print(line_fitter.intercept_)
``````
1 Like

They have hidden the terminal output, and hence you can’t see the values printed to screen.