Hyperparameters are settings that can be tuned to control the behavior of a

machine/deep learning algorithm.

In a Clustering algorithm

the radius of the cluster is one hyperparameter

In neural networks ,

learning rate is a one

Prepare a grid of the possible combination of values of your hyper-parameter!!

Calculate Accuracy for all those combinations

Choose the combination for best accuracy

Prepare a grid of the possible combination of values of your hyperparameter !!

But dont check for every combination !!

Good !! Obviously Faster than Grid ..

But everything is upto luck !!!

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If you want to do a Regression Analysis

In sklearn library,there are around 20 approaches for Regression

On an average each has 5 hyperparameters

Taking 1000 sets of hyper-parameter for each algorithm, if each takes half a minute to train

It would take around 7 days to get you the best hyper-parameter for your data

Do the Math!

And that was not even a Neural Network

Do you remember Decision Tree ??

Good !!

We have something similar for you !!

TPE creates

the tree structure

based on history

Picks the best model

(one with max value of Expected Improvement)

Adds it in its history

Selecting next set of Hyper-param accordingly

Set a range :

let

x be [ -3,1]

y be [ -2,2]

Set the Objective Function:

f(x,y) = x^2 -y^2

best = fmin(f(x,y), space, algo=tpe)

Boom !! You'll get the

best range for x and y

By Tanay Agrawal