Cost function

Introduction

In machine learning, a cost function is a function that is used to optimize a model's parameters by minimizing the error between the predicted output and the actual output. The cost function is used in training a machine learning model to find the set of parameters that minimizes the error between the predicted output and the actual output. The cost function is typically defined as a function of the model's parameters and the training data, and it is used to guide the optimization process by providing a measure of how well the model is doing on the training data.

Explain like I'm in 5th grade

Imagine you are baking a cake and you have a recipe that tells you how much sugar, flour, eggs and other ingredients to use. The recipe is like a plan that tells you how to make the cake. Now, let's say you accidentally put too much sugar in the cake, it's going to taste too sweet and might not be as good as it should have been.

A cost function is like a recipe for a machine learning model, it tells the model how to make predictions. The cost function helps the model learn from its mistakes and improve its predictions. Just like the cake, if the model makes a mistake, the cost function will tell how much of a mistake it made and how to fix it to make the predictions better.

So, just like the cake recipe, the cost function is a set of instructions that helps the model learn from its mistakes and get better at making predictions.

Give a detailed account including reasons or causes.