## Testing the model using the K-fold cross-validation technique

The K-fold cross-validation technique consists of assessing how good the model will be on an independent dataset.

To test the model, the dataset is split into `k`

subsets and the
Random forest algorithm is ran `k`

times:

- At each iteration, one of the
`k`

subsets is retained as the validation set and the remaining`k-1`

subsets are the training set. - A score for each of the
`k`

runs is computed and then the scores obtained are averaged to calculate a global score.

## Tuning the Random forest algorithm hyper-parameters using grid search

You can specify values for the two Random forest algorithm hyper-parameters:

- The number of decision trees
- The maximum depth of a decision tree

To improve the quality of the model and tune the hyper-parameters, grid search builds models for each combination of the two Random forest algorithm hyper-parameter values within the limits you specified.

For example:

- The number of trees ranges from
`5`to`50`with a step of`5`; and - the tree depth goes from
`5`to`10`with a step of`1`.

In this example, there will be 60 different combinations (10 × 6).

Only the best combination of the two hyper-parameters values used to train the best model is retained. This measure is reported by the K-fold cross-validation.