Exploring the Cost Functions

Hi, I am going to share with you how the learning process works.

This article is the Second of the many (I don’t know how many articles i will write) in which i explain to you how the learning process works. 

I hope that this helps you understand in depth how Machine learning works. 

Let's go. 

Exploring the Cost Functions

So start with understanding what is the Cost Function, in fact what is their purpose?. The cost function is an evaluation function that measures how well the machine learning algorithm maps the target function. Also, a cost function determines how well a machine learning algorithm performs in a supervised prediction or an unsupervised optimization problem.

The cost function works by comparing the algorithm predictions against the actual outcome recorded from the real world. It determines the algorithm's error level.

The cost function expresses the error level in a numeric form, a cost value that has to be minimized.

In summary, the cost function is what truly drives the success of a machine learning application.

With respect to your target, a good practice is to define the cost function that works the best in solving the problem, and then to figure out which algorithms work best in optimizing it to define the hypothesis space you want to test.

The optimization process continues as the cost function response improves iteration by iteration. When the response stalls or worsens, it's time to stop because the algorithm likely can't achieve better prediction.

The cost function helps you evaluate whether it's working properly and is indeed effective.

Finally, deciding on the cost function is an underrated activity in machine learning. It's a fundamental task because it determines how the algorithm behaves during the learning phase and how it handles the problem you want to solve. Never ealy on default options.

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Enrique Suárez

TinyML & OT Data Product Development Leader.

I see you in the next article.