The problem of overfitting algorithm
Regularization : a way to decrease overfitting problem

high bias(underfit) : misinterpreted line fit for the data
high variance(overfit) : lot of features (many high order of polynomials)  but lack more data to give a good hypothesis

example for logistic regression
there is a tool for analyzing whether the algorithm has overfitting or underfitting...

a lot of features may risk a lot of high order polynomials....
making it even harder to visualize (in case of over 100 features)