- Home
- Assessment

85 / 94

You can use sklearn's `cross_val_score()`

like-

```
cross_val_score(estimator, predictors_data, target_variable, scoring = None, cv = None)
```

*where*

`estimator`

is our ML model,

`scoring`

is the evaluation metric that we specify (Refer to metrics for seeing a list of available metrics in sklearn for the `scoring`

parameter) and

`cv`

is the value of `k`

in the k-fold.

We use `neg_root_mean_squared_error`

as the `scoring`

metric for our task. It is negative RMSE. Sklearn's cross-validation features uses a utility function instead of a cost function. In the cost function, the cost will be lower for a better model while in a utility function, it should be greater for a better model.

And because of this convention of `sklearn`

to use a utility function while cross validating, we use the scoring function as negative RMSE. It is the opposite of the RMSE. So, negative RMSE is just a negative version of the numbers which we get in RMSE. So, if for one data point, RMSE comes as 3 then negative RMSE will be -3 for that.

Refer to cross_val_score documentation for further details about the method.

Import function

`cross_val_score`

from`sklearn.model_selection`

.Use the

`cross_val_score`

function and provide`tree_reg`

as*estimator*,`housing_prepared`

and`housing_labels`

as*predictors_data*and*target_variable*,`neg_root_mean_squared_error`

as the*scoring*metric and*cv*as`10`

for parameters as we want to perform 10-fold cross-validation. Store the output in a variable named`scores`

.The scores will be negative. Pass them through

`abs()`

function to convert them in positives by-`scores = abs(scores)`

**Note-**Scores will be different on different runs due to the stochastic nature of the function.

XP

Taking you to the next exercise in seconds...

Want to create exercises like this yourself? Click here.

Checking Please wait.

Success

Error

Fetching hint, please wait...

Error

Fetching answer, please wait...

Error

**Note - **Having trouble with the assessment engine? Follow the steps listed
here

## Loading comments...