# Classification with PyCaret

Learn how to import necessary libraries and datasets for classification with PyCaret.

## We'll cover the following

**Classification** is one of the fundamental supervised learning tasks. Its goal is to predict a categorical variable known as the class label. This task is known as binary classification when there are only two classes ($0$ and $1$), or **multiclass classification** in case there are more. One of the most widely used binary classification models is **logistic regression**. It is defined in the following equation:

$\log (\frac{p_{n}}{1-p_{n}})=\beta_{0}+\beta_{1} x_{n 1}+\cdots+\beta_{p} x_{n p}=\beta^{T} X_{n}$

- $\log (\frac{p_{n}} {1-p_{n}})$ is the natural logarithm of the odds, known as the
.**logit function** - $x_1$ to $x_p$ are the feature variables.
- $\beta_{0}$ is the
term.**intercept** - $\beta_{1}$ to $\beta_{p}$ are the coefficients of the feature variables.
- $\beta^{T} X_{n}$ is the vectorized form of the equation.
Our goal is to calculate $p_{n}$ which is the probability that an instance of the given dataset belongs to class $1$. The
**logistic function $\sigma(z)$**is the inverse of the logit (or log-odds) function, so we can apply it and get the desired result.

$p_{n}=\sigma (\beta^{T} X_{n})=\frac{1}{1+\exp (-\beta^{T} X_{n})}$

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