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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.

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