When trying to evaluate the accuracy of our multiple linear regression model, one technique we can use is **Residual Analysis**.

The difference between the actual value *y*, and the predicted value *ŷ* is the **residual e**. The equation is:

`$e = y - \hat{y}$`

In the StreetEasy dataset, *y* is the actual rent and the *ŷ* is the predicted rent. The real *y* values should be pretty close to these predicted *y* values.

`sklearn`

‘s `linear_model.LinearRegression`

comes with a `.score()`

method that returns the coefficient of determination R² of the prediction.

The coefficient R² is defined as:

`$1 - \frac{u}{v}$`

where *u* is the residual sum of squares:

((y - y_predict) ** 2).sum()

and *v* is the total sum of squares (TSS):

((y - y.mean()) ** 2).sum()

The TSS tells you how much variation there is in the y variable.

R² is the percentage variation in y explained by all the x variables together.

For example, say we are trying to predict `rent`

based on the `size_sqft`

and the `bedrooms`

in the apartment and the R² for our model is 0.72 — that means that all the x variables (square feet and number of bedrooms) together explain 72% variation in y (`rent`

).

Now let’s say we add another x variable, building’s age, to our model. By adding this third relevant x variable, the R² is expected to go up. Let say the new R² is 0.95. This means that square feet, number of bedrooms and age of the building *together* explain 95% of the variation in the rent.

The best possible R² is 1.00 (and it can be negative because the model can be arbitrarily worse). Usually, a R² of 0.70 is considered good.

### Instructions

**1.**

Use the `.score()`

method from `LinearRegression`

to find the mean squared error regression loss for the **training set**.

Write that number down.

**2.**

Use the `.score()`

method from `LinearRegression`

to find the mean squared error regression loss for the **testing set**.

Write that number down.

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