Why do we take square of error?

MSE is used to check how close estimates or forecasts are to actual values. Lower the MSE, the closer is forecast to actual. This is used as a model evaluation measure for regression models and the lower value indicates a better fit.

Why do we divide MSE by 2?

It is because when you take the derivative of the cost function, that is used in updating the parameters during gradient descent, that 2 in the power get cancelled with the 12 multiplier, thus the derivation is cleaner.

What is error function in gradient descent?

Gradient Descent is a general function for minimizing a function, in this case the Mean Squared Error cost function. Gradient Descent basically just does what we were doing by hand — change the theta values, or parameters, bit by bit, until we hopefully arrived a minimum.

Can mean square error be negative?

MSE is a risk function, corresponding to the expected value of the squared error loss. The fact that MSE is almost always strictly positive (and not zero) is because of randomness or because the estimator does not account for information that could produce a more accurate estimate.

How error value is calculated using gradient descent method?

The intuition behind Gradient Descent Algorithm For a given problem statement, the solution starts with a Random initialization. These initial parameters are then used to generate the predictions i.e the output. Once we have the predicted values we can calculate the error or the cost.

Why we use Square in linear regression?

R-squared evaluates the scatter of the data points around the fitted regression line. For the same data set, higher R-squared values represent smaller differences between the observed data and the fitted values. R-squared is the percentage of the dependent variable variation that a linear model explains.

Why mean squared error is not used as a loss function in Binomial Logistic Regression?

Why doesn’t MSE work with logistic regression? One of the main reasons why MSE doesn’t work with logistic regression is when the 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.