Parallel grid search

Grid search calculation grows exponentially with each parameter and its possible values we want to tune. We could reduce our response time if we calculate each of the combinations in parallel instead of sequentially, as we have done. In our previous example, we had four different values for gamma and three different values for C, summing up 12 parameter combinations. Additionally, we also needed to train each combination three times (in a three-fold cross-validation), so we summed up 36 trainings and evaluations. We could try to run these 36 tasks in parallel, since the tasks are independent.

Most modern computers have multiple cores that can be used to run tasks in parallel. We also have a very useful tool within IPython, ...

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