bias and variance calculation for linear regression
Evaluation of Variance:
Variance = np.var(Prediction) # Where Prediction is a vector variable obtained post the 
                              # predict() function of any Classifier.
SSE = np.mean((np.mean(Prediction) - Y)** 2) # Where Y is your dependent variable.
                                             # SSE : Sum of squared errors. 
Bias = SSE - Variance
If we have 4 parameters of X_train, y_train, X_test, and y_test, how can we calculate the bias and variance of a machine learning algorithm like linear regression?
I have searched a lot but I could not find a single code for this.