ruk·si

# Regressions

Updated at 2017-11-13 14:39

Regression analysis is a statistical process for estimating the relationships among variables.

In regression machine learning problems, the desired output is a continuous number e.g. age of a person.

• Simple Linear Regression: a linear predictor function with one input variable e.g. `(y = x^2)`.
• Multiple Linear Regression: a linear predictor function with more than one input variable.
• Ordinal Regression: model output is an ordinal variables thus have an order but unknown distance between themselves e.g. "good", "ok", "poor".
• Nonlinear Regression: the predictor function is nonlinear such as exponentiation^2. There may be multiple local minima to optimize.
• General Linear Model: a matrix formula (`y = xb + u`) which has input matrix `x`, output matrix `y`, model coefficiency matrix (`b`) and error matrix `u`. `b` works like neural network weights and `u` like bias, finding the right matrix values for those will allow creating predictions.
• Generalized Linear Model (GLM): allows for outputs that have error distribution models other than a normal distribution by allowing the linear model to be related to the response variable via a link function.

# Logistic Regressions

• Logistic Regression predictions are categorical.
• Binary Logistic Regression predictions are either 0 or 1.
• Multinomial Logistic Regression predictions can be more than 2 possible discrete outcomes.
• Ordered Logistic Regression predictions are ordinal e.g. "poor", "fair" and "good".