Generalization

Introduction

Generalization is a term used in machine learning to describe the ability of a model or algorithm to perform well with unknown data. It is an important aspect of machine learning because the ultimate goal is to create models that can adapt well to new data rather than just memorizing the training data.

The Problem of Overfitting

Overfitting is a common problem in machine learning. This is when a model is too complex and performs well on training data but poorly on unseen data. This happens because the model is learning the noise from the training data rather than the underlying relationship. Regularization techniques such as L1 regularization and L2 regularization can be used to reduce overfitting. This is done by adding a penalty to the loss function that encourages the model to have smaller weights.

Cross-Validation

Another technique used to evaluate the generalization performance of a model is cross-validation. This involves dividing the data into multiple "folds" and training a model on different subsets of data while evaluating its performance with the rest. This allows for a more reliable estimate of the model’s performance on unseen information.

Explain Like I'm 5 (ELI5)

"Generalization in machine learning" refers to making a computer program that can handle new examples. A teacher might train a student to solve math problems. However, the student should be able to answer questions they haven’t seen before. Cross-validation and regularization are used to prevent the computer program from memorizing the examples it was taught and not being able to work with new examples. These techniques ensure that the computer program can generalize well and work well using new examples.