Perceptron Classifier

The Perceptron Classifier is a linear classification model based on a simple artificial neural unit. It iteratively adjusts weights in the input features to find a hyperplane that separates two classes. It is well-suited for high-dimensional data and is one of the simplest types of classifiers.

Advantages:

• Simple and easy to understand
• Fast to train
• Works well with high-dimensional data
• Low computational requirements

Disadvantages:

• Sensitive to feature scaling
• Not suitable for non-linearly separable data
• May not converge if data is not linearly separable
• Lacks probabilistic interpretation