What is Hyper Parameter Tuning in Machine Learning?

Hyper parameter in machine learning

What is Hyper Parameter Tuning? 

In machine learning, tuning or hyper parameter optimization is the difficulty of picking a collection of optimal parameters for a model learning algorithm. A hyper Parameter is also called a model predictor, since its value is used as a starting point for the model learning algorithm. Unlike the normal parameters, the numerical values of hyper parameters are learned as the training progresses. The choice of the hyper Parameter should be made carefully in order not to make model errors at the end of the training.

  • Uses of Hyper Parameter Tuning 
  • Application of Hyper Parameter Tuning 

Use of Hyper Parameter Tuning: 

In real life situations, it would not be possible to create a hyper parametric model that would work equally well in all situations. Thus the designers of these models come up with a range of numbers which can be tuned until they produce the best results. The numbers chosen will depend on the exact requirements of the task. If there are no restrictions, a number as large as 10 can be used. When the desired accuracy is attained, the best possible numbers can be used.

However, even with this level of generality, many questions remain unanswered about how to tune hyperparameters. How can one tune a parameter that has not been determined prior to training? Why does it matter how many hyperParameters are used in training? These are only a few of the issues that arise when we try to understand how to tune hyperparameters in machine learning models.

Application of Hyper Parameter Tuning: 

To begin, it is important to realize that the hyper Parameter, in the form of a hyperParametric function, is just a mathematical tool. It is not a specification or a rule that can be followed to solve any design problem. Many designers and researchers use hyperparameters to achieve objectives in their designs. In fact, when a designer implements a hyper parametric function in a training set, the resulting model is very different from its prediction. The reason for this is that during training the hyper parametric function was applied many times and each time it was tuned, a new objective was achieved.

This means that even the most optimized hyper Parameters in a given training set will not provide the desired accuracy. This problem is addressed by the designers of the model with a tuning procedure. After each pass through the network, the model can make corrections to the hyper Parameter in order to bring back a model with an accurate prediction.

For example, if there are two parameters, A and B, that determine the value of the mean square of the data values, then the model will make adjustments in order to obtain the best value for A and B. Of course, many network designers already know that tweaking the hyperparameters is necessary in order to achieve the desired accuracy. The designers need to choose how often they will tweak the hyper Parameters. If they find that many parameters need to be changed, then they will keep those parameters unchanged. If they find that many new parameters need to be introduced into the model, then they may introduce those parameters, too. There is no exact right or wrong answer for these decisions, just as there are no right and wrong answer for the other things that go on in a hyper Parameter learning training session.


The purpose of a hyper-Parameter model is to provide a fast learning environment. It is an important ingredient of many current Machine Learning Software packages. Many of the newer packages come with wide tuning capabilities so that they can be effectively used in many situations where traditional tuning may not be feasible. Wide tuning makes many current problems, where predicting the parameters of interest using traditional methods, much easier to solve. It is not just an interesting theory. It is an important ingredient of many current Machine Learning Software packages. When you want to start a new project in Machine Learning, you should definitely consider adding a hyper-Parameter model to your toolbox.

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