The idea behind `RandomizedSearchCV` is that it works the same as `GridSearchCV`, but instead of trying all the combinations, it tries a **randomly sampled subset**. 

For example, this `param_grid` will have 100 combinations:
```python
param_grid = {'n_neighbors': [1, 3, 5, 7, 9, 12, 15, 17, 20, 25],
                         'weights': ['distance', 'uniform'],
                         'p': [1, 2, 3, 4, 5]}
```
The `GridSearchCV` would try all of them, which is **time-consuming**. With `RandomizedSearchCV`, you can try only a **randomly chosen subset** of, say, 20 combinations.  It usually leads to a little worse result, but works much faster. 

You can control the number of combinations to be tested using the `n_iter` argument (set to **10** by default). Apart from that, working with it is the same as with `GridSearchCV`.

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Challenge: Tuning Hyperparameters with RandomizedSearchCV

Lösung

Challenge: Tuning Hyperparameters with RandomizedSearchCV

Lösung