Hypothesis function = prediction function.

Example: linear regression (i.e. graph of the values predicted by linear regression function is a line). The formula for this function is a function of a line. H(x) = a + b*x

Cost function - the function to check accuracy of our hypothesis function i.e. the accuracy of our prediction (to get the best possible line in a linear model).

Cost function in linear regression = sum of squared errors

= total of (difference in actual value and predicted value)^2 for the entire dataset

Watch this space for more updates!

Example: linear regression (i.e. graph of the values predicted by linear regression function is a line). The formula for this function is a function of a line. H(x) = a + b*x

Cost function - the function to check accuracy of our hypothesis function i.e. the accuracy of our prediction (to get the best possible line in a linear model).

Cost function in linear regression = sum of squared errors

= total of (difference in actual value and predicted value)^2 for the entire dataset

Watch this space for more updates!

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