Why MSE is not good for classification?

There are two reasons why Mean Squared Error(MSE) is a bad choice for binary classification problems: When the MSE function is passed a value that is unbounded a nice U-shaped (convex) curve is the result where there is a clear minimum point at the target value (y).

Does logistic regression use mean squared error?

The squared error function (commonly used function for linear regression) is not very suitable for logistic regression. As in case of logistic regression the hypothesis is non-linear (sigmoid function), which makes the square error function to be non-convex.

What is the rationale behind the cost function for logistic regression?

The Cost Function is important because it gives us the errors of our predictions and subsequently, is needed for our learning algorithm. Concretely, we like to minimise the errors of our predictions, i.e, to minimise the cost function.

Why cost function which has been used for linear can not be used for logistic?

The hypothesis of logistic regression tends it to limit the cost function between 0 and 1. Therefore linear functions fail to represent it as it can have a value greater than 1 or less than 0 which is not possible as per the hypothesis of logistic regression.

Is MSE a loss function?

Mean squared error (MSE) is the most commonly used loss function for regression. The loss is the mean overseen data of the squared differences between true and predicted values, or writing it as a formula.

Why cost function is squared?

A squared-error cost function is designed to reflect a penalty for an error in our estimate that increases with the square of the difference between our estimate and the actual value.

How is regularization useful in avoiding Overfitting?

Regularization is a technique that adds information to a model to prevent the occurrence of overfitting. It is a type of regression that minimizes the coefficient estimates to zero to reduce the capacity (size) of a model. In this context, the reduction of the capacity of a model involves the removal of extra weights.

Is logistic regression a convex problem?

Now, since a linear combination of two or more convex functions is convex, we conclude that the objective function of logistic regression is convex.

Can cost function be negative?

3 Answers. In general a cost function can be negative. The more negative, the better of course, because you are measuring a cost the objective is to minimise it. A standard Mean Squared Error function cannot be negative.

Why is MSE a good loss function?

Advantage: The MSE is great for ensuring that our trained model has no outlier predictions with huge errors, since the MSE puts larger weight on theses errors due to the squaring part of the function. Disadvantage: If our model makes a single very bad prediction, the squaring part of the function magnifies the error.