Contents
Why is the squared loss not suitable for binary classification?
There are two reasons why Mean Squared Error(MSE) is a bad choice for binary classification problems: If we use maximum likelihood estimation(MLE), assuming that the data is from a normal distribution(a wrong assumption, by the way), we get the MSE as a Cost function for optimizing our model.
Why can’t we use mean square error MSE as a cost function for logistic regression?
Mean Squared Error, commonly used for linear regression models, isn’t convex for logistic regression. This is because the logistic function isn’t always convex. The logarithm of the likelihood function is however always convex.
Which is better log loss or mean squared error?
In this blog post, we mainly compare “ log loss ” vs “mean squared error” for logistic regression and show that why log loss is recommended for the same based on empirical and mathematical analysis. Equations for both the loss functions are as follows:
How is the log loss function used in linear regression?
The loss function used by the linear regression algorithm is Mean Squared Error. Mean squared error formula What MSE does is, it adds up the square of the distance between the actual and the predicted output value for every input sample (and divide it with no.of input samples). Using MSE as an error function gave very good results.
Least Square Regression models the relationship between a dependent variable and a collection of independent variables. The value of a dependent variable is defined as a linear combination of the independent variables plus an error term ϵ. where (B0 …
Is the MSE loss function for logistic regression convex?
Now we mathematically show that the MSE loss function for logistic regression is non-convex. For simplicity, let’s assume we have one feature “x” and “binary labels” for a given dataset.