Why mean squared error is not used in logistic regression?
One of the main reasons why MSE doesn’t work with logistic regression is when MSE loss function is plotted with respect to weights of the logistic regression model, the curve obtained is not a convex curve which makes it very difficult to find the global minimum.
Why do we minimize the sum of squared errors?
In econometrics, we know that in linear regression model, if you assume the error terms have 0 mean conditioning on the predictors and homoscedasticity and errors are uncorrelated with each other, then minimizing the sum of square error will give you a CONSISTENT estimator of your model parameters and by the Gauss- …
What is the cost function used in logistic regression?
Log Loss
The cost function used in Logistic Regression is Log Loss.
Which is easier to solve least squares or logistic regression?
To solve least squares numerically will likely take longer. learning parameters for any machine learning model (such as logistic regression) is much easier if the cost function is 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:
Why is MSE not used in logistic regression?
Before diving deep into why MSE is not a convex function when used in logistic regression, first, we will see what are the conditions for a function to be convex. A real-valued function defined on an n -dimensional interval is called convex if the line segment between any two points on the graph of the function lies above or on the graph.
Is the log loss function convex for logistic regression?
We will mathematically show that log loss function is convex for logistic regression. Theta: co-efficient of independent variable “x”. As seen in the final expression (double derivative of log loss function) the squared terms are always ≥0 and also, in general, we know the range of e^x is (0, infinity).