# Linear Methods

## #Classification #Supervised Learning #Statistical Learning #Basics #Linear Models

## Solving Classification Problems with Linear Models

One simple idea behind classification is to calculate the posterior probability of each class given the variables.

Suppose a dataset have features $F_\alpha$ where $\alpha = 1, 2, \cdots, K$, with corresponding class labels $G_\alpha$. The dataset that provides $N$ datapoints with each deoted as $X_i$. The posterior of the classification is $P(G = G_\alpha \vert X = X_i)$.

A naive idea is to classify the data into two classes $m$ and $n$ using the boundary of a linear model

$$ P(G = G_\alpha \vert X = X_i) = P(G = G_\beta \vert X = X_i). $$

For a linear model, the boundary is a hyperplane in the feature space, $f(x_1, x_2, \cdots, x_K) = 0$.

Is this linear model terribly bad because it is linear? Not necessarily. With feature engineering, such as feature crossing, a linear separation plane in the engineered feature space can be mapped to complicate boundaries in the original feature space.

## Table of Contents

**Current Ref:**

- wiki/machine-learning/linear/linear-methods.md