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.
L Ma (2018). 'Linear Methods', Datumorphism, 05 April. Available at: https://datumorphism.leima.is/wiki/machine-learning/linear/linear-methods/.