- Email spam detection (spam or not).
- Churn prediction (churn or not).
- Conversion prediction (buy or not).

University of Michigan - Ann Arbor

Binary classification is the simplest form of classification problem where there are exactly two possible classes or categories to predict. A common example is designing a classifier that takes an image as input and outputs the predicted class assignment from a set of two options, such as $$\textrm{\{cat, dog\}}$$.

Binary Classification

Developers need to know what works and how to use it. This site uses less math and more tutorials with working code.
https://machinelearningmastery.com/

Machine Learning Mastery

Examples of Binary Classification

Logistic regression is a statistical model that forces the output of an otherwise unbounded function to the interval (0,1).

$$p(y=1 | x;\theta) = \sigma(\theta^\top x) $$
$$\sigma(x) = \frac{e^x}{e^x + 1}$$
$$p(y=0 | x;\theta) = 1 - p(y=1 | x;\theta)$$

Where $\theta$ is the parameter vector.

For binary classification problems, logistic regression can be used instead of linear regression to handle the discontinuous nature of the target output.

However, the sigmoid function $\sigma$ has no local minimum or maximum, so a technique like gradient decent must be used instead.

Logistic Regression

Logistic regression is used to model discontinuous output choices - for example, when a variable is 'yes' or 'no' rather than a continuous value. In this case, logistic regression calculates the probability of a given categorical output, and decides on a threshold at which the model decides between categories to select for output. 

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