Hi. In this video we will learn more about tune search Cross validation.
Tune search is a randomized search from library called Array,
and it's main benefit is that in addition to purely randomized
search in a sticky learn, we can specify various optimizers.
So basically the search for the best hyper parameters may not be
completely random,
but there is some logic behind it where some parameter combinations
are thrown away because behind the scenes,
the optimizer evaluates that continuing exploring parameters in a
specific direction doesn't make sense and that some combination of
parameters are more promising to explore more.
Using Ray,
we can also scale our hyper parameter optimization to multiple
machines as well. Here,
uh, we put more parameters, uh,
to test and replace the sampling distributions from SciPi Library
to tune methods. Instead of, um,
an inter parameter,
we specify number of trials and we may also specify,
uh, the optimizer. In this case,
I choose to use the bio and optimization with Hyper Bend,
but tune search supports more of them. For example,
a pure bio and optimizer from a circuit optimized package,
Opta never ground, and others
we call the feed method and we can check what was the best parameters and we can
evaluate the model on this data.
In our next video, we will learn more about the light G B M algorithm.