Working with model objects

1. Working with model objects

The model objects created by ols contain a lot of information. In this video, you'll see how to extract it.

2. .params attribute

You already learned how to extract the coefficients or parameters from your fitted model. You add the dot params attribute, which will return a pandas Series including your intercept and slope.

3. .fittedvalues attribute

"Fitted values" is jargon for predictions on the original dataset used to create the model. Access them with the fittedvalues attribute. The result is a pandas Series of length thirty five, which is the number of rows in the bream dataset. The fittedvalues attribute is essentially a shortcut for taking the explanatory variable columns from the dataset, then feeding them to the predict function.

4. .resid attribute

"Residuals" are a measure of inaccuracy in the model fit, and are accessed with the resid attribute. Like fitted values, there is one residual for each row of the dataset. Each residual is the actual response value minus the predicted response value. In this case, the residuals are the masses of breams, minus the fitted values. I illustrated the residuals as red lines on the regression plot. Each vertical line represents a single residual. You'll see more on how to use the fitted values and residuals to assess the quality of your model in Chapter 3.

5. .summary()

The summary method shows a more extended printout of the details of the model. Let's step through this piece by piece.

6. .summary() part 1

First, you see the dependent variable(s) that were used in the model, in addition to the type of regression. You also see some metrics on the performance of the model. These will be discussed in the next chapter.

7. summary() part 2

In the second part of the summary, you see details of the coefficients. The numbers in the first column are the ones contained in the params attribute. The numbers in the fourth column are the p-values, which refer to statistical significance. You can learn about them in DataCamp's courses on inference. The last part of the summary are diagnostic statistics that are outside the scope of this course.

8. Let's practice!

Your turn to extract some model elements.

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