Classification is a type of supervised learning where the goal is to predict a categorical output variable based on input features.
The input features can be continuous, discrete, or a mixture of both.
The categorical output variable can be binary, such as yes or no, or multi-class
The performance of the model is typically measured using metrics such as accuracy, precision, recall, and F1 score.
There are many classification algorithms available, including logistic regression, decision trees, random forests, SVM, etc.
Feature engineering is an important aspect of classification, where the input features are transformed to make them more informative for the model.
Some common feature engineering techniques include scaling, normalization, one-hot encoding, and feature selection.
Classification has many practical applications, including image recognition, speech recognition, natural language processing, and fraud detection.
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