Regression algorithms are a type of supervised algorithm that uses features of the input data to predict a value, such as the cost of a house, given certain features, such as size, age, number of bathrooms, number of floors, and location. Regression analysis tries to find the value of the parameters for the function that best fits an input dataset.
In a linear-regression algorithm, the goal is to minimize a cost function by finding appropriate parameters for the function, over the input data that best approximates the target values. A cost function is a function of the error, that is, how far we are from getting a correct result. A popular cost function is the mean square error (MSE), where we take the square ...
