# Non-linear hypothesis

- Outdated but most powerful learning algorithm in most ML problems

# Regularized Logistic Regression

- regularized both gradient descent of cost function and the more advanced optimization that includes cost function and derrivative

# Regularized Linear Regression

- regularized both gradient descent and normal equation algorithms for linear regression

# The problem of overfitting

The problem of overfitting algorithm

Regularization : a way to decrease overfitting problem

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)