| [1.39s -> 13.30s] so what we actually did in the previous diagram was to find out the mean squared error that is the difference between the actual y values and the predicted y values |
| [14.06s -> 21.78s] So the main purpose of doing that was to find out m and b, that are the weights that we'll need for finding out a prediction line. |
| [22.96s -> 36.91s] Now, there can be different cost functions depending on our function, but for the purpose of linear regression, we use mean squared error. Mean squared error measures the average squared difference between an observation's actual and predicted values. |
| [37.01s -> 50.68s] The output is a single number representing the cost or score that are associated with our current set of weights. Our goal is to minimize MSE and to improve the accuracy of our model. |
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