Grid search for parameter tuning

Grid search for parameter tuning

Grid search for parameter tuning. Learn this easy and simple technique to tune your Machine Learning models. GridSearchCV can be used with any supervised learning Machine Learning algorithm that is in the sci-kit learn library.

Introduction

Once you have built a machine learning model you would like to tune its parameters for optimal performance. The best parameters would be different for each data set therefore they need adjusting so the algorithm can gain its maximum potential.

I have seen many beginner data scientists doing parameter tuning by hand. This means running the model, then changing one or multiple parameters within the notebook, waiting for the model to run, gathering results, and then repeating the process again and again. Usually, people forget on the way which parameters were the best and they need to do it again.

In general, the above strategy is not the most efficient. Luckily this process could be easily was automated thanks to the authors of the sci-kit learn library who added GridSeachCV.

What is GridSearchCV?

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Image by Nicolás Damián Visceglio from Pixabay

GridSearchCV is an alternative to the naive method I have described above. Instead of manually tweaking the parameters and rerunning the algorithm several times you can list all parameter values that you would like the algorithm try and pass it to GridSeachCV.

GridSearchCV will try all combinations of those parameters, evaluate the results using cross-validation, and the scoring metric you provide. In the end, it will spit the best parameters for your data set.

GridSearchCV can be used with any supervised learning Machine Learning algorithm that is in the sci-kit learn library. It will work both for regression and classification if you provide an appropriate metric.

Let’s see how it works with a real example.

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