Hypothesis Space and Cost Function IN SHORT

Hypothesis space

Hypothesis are trained models and Space means possibilities.

So hypothesis space means a set of possible models for the given training dataset. It defines all possible parameters that the cost function (model) can assume.

For example, if a customer will invest in Fixed Deposit (Yes/No)?

Suppose, we have three columns Gender (Male/Female), Job (Yes/No) and Married (Yes/No). So possible combinations will be 2³ = 8. So, the Hypothesis Space for this is equal to 8.

Cost Function

The cost function measures the performance of a machine-learning model. The purpose of the function is to either minimise the cost or maximising the cost.

A scenario of minimising the cost is when the cost function is in terms of error. The higher the error, bad is the machine learning model. So we minimize the cost. For example, regression-based models.

A scenario of maximising the cost is when the cost function is in terms of rewards. The higher the reward, the better the model. So we maximize the cost. For example, reward-based models.

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