Perceptron is an algorithm for supervised learning of binary classifiers. A binary classifier is a function that can decide whether or not an input, represented by a vector of numbers, belongs to some specific class.
Perceptron is the simplest type of neural network model. It is a model of a single neuron that can be used for two-class classification problems and provides the foundation for later developing much larger networks.
It consists of a single node or neuron that takes a row of data as input and predicts a class label. This is achieved by calculating the weighted sum of the inputs and a bias (set to 1). The weighted sum of the input of the model is called the activation.
- Activation = Weights * Inputs + Bias
If the activation is above 0.0, the model will output 1 otherwise it will output 0.0.
- Predict 1: If Activation > 0.0
- Predict 0: If Activation <= 0.0
Given that the inputs are multiplied by model coefficients, like linear regression and logistic regression, it is good practice to normalize or standardize data prior to using the model.
The perceptron takes a weighted sum of multiple inputs (along with a bias) as the cumulative input and applies a step function on the cumulative input, i.e. it returns 1 if the input is positive, else -1. In other words, the perceptron returns 1 if the cumulative input is positive and “stays dormant” (returns 0) if the input is negative.