Batch

Introduction

Batch learning, also referred to as "offline learning," is a type of machine learning in which data is processed in batches rather than real-time or online. With this approach, the model is trained using historical data and then applied to make predictions on new data sets.

Background

Machine learning encompasses two primary approaches: supervised and unsupervised. Supervised learning involves training a model on labeled data, where both inputs and outputs are known; unsupervised learning takes advantage of an unknown dataset where inputs and outputs may not be known. Batch learning is another type of supervised learning where models are trained using large amounts of historical information before being applied to new data sets that have yet to be observed.

Advantages

Batch learning offers several advantages. One major benefit is its capacity for handling large amounts of data, leading to more precise and resilient models. Furthermore, batch learning enables more complex models like deep neural networks which would otherwise require training in real-time. Furthermore, batch learning enables computationally expensive techniques like grid search or cross-validation which may improve model performance.

Disadvantages

Batch learning has its advantages, but it also has some drawbacks. One major issue is that it cannot be used for real-time applications where predictions must be made quickly and on-the-spot. Furthermore, batch learning requires large amounts of data which may be difficult or expensive to obtain. Furthermore, batch learning requires computationally expensive models which need to be trained on a large dataset before being applied to new data sets.

Explain Like I'm 5 (ELI5)

Batch learning is a method of instructing a computer how to do something by providing it with many examples at once, similar to giving a student their test after they have studied an entire chapter instead of one problem at a time. While batch learning can make the computer better at certain tasks, it requires many examples and may be slow in some cases.