Classical learning, also known as supervised learning, is a type of machine learning in which a model is trained to predict an output based on a set of input features. The process typically involves a dataset that includes input-output pairs, where the model is trained to learn the relationship between the inputs and the corresponding outputs.

The goal of classical learning is to find a general rule, or a function, that maps inputs to outputs. This function is usually represented by a mathematical model, such as a linear regression, decision tree, or neural network. The model is trained using a training dataset, which includes a set of input-output pairs. The model is then evaluated on a separate test dataset to measure its performance.

The training process involves adjusting the parameters of the model to minimize the error between the predicted outputs and the true outputs. This is done using an optimization algorithm, such as gradient descent, that iteratively updates the parameters to reduce the error.

There are many different types of classical learning algorithms, each with their own strengths and weaknesses. Some of the most common types include:

Linear regression: used to predict a continuous output
Logistic regression: used to predict a binary output
Decision trees: used to make decisions based on a set of input features
Random forests: an ensemble of decision trees
Neural networks: used to model complex relationships between inputs and outputs.
Classical learning algorithms are widely used in many applications such as speech recognition, image classification, natural language processing, and prediction.