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`.

Machine learning is now used everywhere. Want to learn it yourself? This course is an introduction to the world of Machine learning for you to learn basic concepts, work with Scikit-learn – the most popular library for ML and build your first Machine Learning project.
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Challenge: Tuning Hyperparameters with RandomizedSearchCV

Løsning

Awesome!

Challenge: Tuning Hyperparameters with RandomizedSearchCV

Løsning

Awesome!