Hyperopt - A bayesian Parameter Tuning Framework

Recently I was working on a in-class competition from the How to win a data science competition; Coursera course. You can start for free with the 7-day Free Trial. Learned a lot of new things from that about using XGBoost for time series prediction tasks.

The one thing that I tried out in this competition was the Hyperopt package - A bayesian Parameter Tuning Framework. And I was literally amazed. Left the machine with hyperopt in the night. And in the morning I had my results. It was really awesome and I did avoid a lot of hit and trial.

What really is Hyperopt?

From the site:

Hyperopt is a Python library for serial and parallel optimization over awkward search spaces, which may include real-valued, discrete, and conditional dimensions.

What the above means is that it is a optimizer that could minimize/maximize the loss function/accuracy(or whatever metric) for you.

All of us are fairly known to cross-grid search or random-grid search. Hyperopt takes as an input a space of hyperparams in w…