# Lets Get Started

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

# How Do We Tune Them

## Exhaustive Approach!!

### Grid Search

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

## Random Search

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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# Just Think !!

`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

### Damn its slow

Do the Math!

```And that was not even a
Neural Network```

## Tree-Structured Parzen Estimator

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)

Selecting next set of Hyper-param accordingly

# So where do the Hyperopt fits in the Picture?

## Such that x and y belongs to a range R1 and R2

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

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