Choosing A Hyperparameter Tuning Library — Ray[tune] Or Aisaratuners?

Choosing A Hyperparameter Tuning Library — Ray[tune] Or Aisaratuners?

Creating a model is easy. But you already know how Choosing a hyperparameter tuning library — ray[tune] or aisaratuners? In the most optimal wayresults…

Benchmarking of two hyperparameter tuners

Creating a model is easy. What’s hard is building a model with optimal hyperparameters!. You can create a neural network with a random number of hidden layers and it will probably give you results better than random. But to get optimal results, you need to get the best hyperparameters that optimize the results. This process of finding the best hyperparameters is known as Hyperparameters tuning

This is basically a time-consuming and a computationally expensive process as we have to search a pretty wide space in order to find these. And also we as humans, can’t try each and every one of the combinations and thus can’t claim a set of hyper parameters as the best ones. Enter hyper parameters tuning libraries. These libraries search the parameters space and calculate the metrics for each one. It lets you know the optimized hyper parameters for your model, and the best thing is you have to do minimal changes in the code.

Two such libraries, that I will be comparing in this article, are *ray[tune] *and *aisaratuners. *I will be testing them on the iris dataset to see which library performs better.

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