Types of cost function in machine learning

1. What is a cost function in ML?
2. Why cost function is used in machine learning?
3. What are different cost functions in machine learning?
4. What are the types of cost function?

What is a cost function in ML?

In ML, cost functions are used to estimate how badly models are performing. Put simply, a cost function is a measure of how wrong the model is in terms of its ability to estimate the relationship between X and y. This is typically expressed as a difference or distance between the predicted value and the actual value.

Why cost function is used in machine learning?

A machine learning parameter that is used for correctly judging the model, cost functions are important to understand to know how well the model has estimated the relationship between your input and output parameters.

What are different cost functions in machine learning?

The cost function is the technique of evaluating “the performance of our algorithm/model”. It takes both predicted outputs by the model and actual outputs and calculates how much wrong the model was in its prediction. It outputs a higher number if our predictions differ a lot from the actual values.

What are the types of cost function?

The types are: 1. Linear Cost Function 2. Quadratic Cost Function 3. Cubic Cost Function.