Items

See also: Machine learning terms

Introduction

In the field of machine learning, the term "items" typically refers to individual data points or instances that are used as inputs to train, validate, or test machine learning models. Items can take various forms, such as images, texts, or numerical values, depending on the specific problem being addressed. This article will explore the concept of items in machine learning, their significance in model training, and their representation in different types of data sets.

Data Sets in Machine Learning

In the context of machine learning, data sets are collections of items used to build and evaluate models. These data sets are generally split into three main categories:

Training Set

The training set is a large subset of the data used to train a machine learning model. It is used to adjust the model's parameters so that it can learn the underlying patterns in the data. The quality and quantity of items in the training set can significantly impact the model's performance.

Validation Set

The validation set is a smaller subset of the data, separate from the training set, used to evaluate the model's performance during training. By comparing the model's predictions on the validation set with the actual outcomes, machine learning practitioners can identify overfitting, underfitting, or other issues that may affect the model's generalization capabilities.

Test Set

The test set is another smaller subset of data, distinct from both the training and validation sets, used to evaluate the model's performance after training is complete. This provides an unbiased estimate of the model's performance on new, unseen data.

Representation of Items in Different Data Types

Items in machine learning can be represented in various ways, depending on the type of data being used.

Tabular Data

In tabular data, items are often represented as rows in a table, with each column corresponding to a feature or attribute of the item. For example, a data set containing information about houses might have columns for square footage, number of bedrooms, and selling price, with each row representing a different house.

Text Data

In text data, items are typically represented as documents or sentences. These can be further broken down into words or tokens, which are then converted into numerical representations (such as word embeddings) for use in machine learning models.

Image Data

In image data, items are represented as digital images, often encoded as multi-dimensional arrays or tensors. Each element in the array corresponds to a pixel in the image, and its value represents the pixel's color or intensity.

Explain Like I'm 5 (ELI5)

Imagine you have a big box of different toys, and you want to teach a robot how to recognize and sort them. In this case, each toy would be an "item" in the world of machine learning. The robot would need to look at many toys (the data) to learn what each one looks like and how to tell them apart. To do this, the robot might study a group of toys (the training set), then practice sorting another smaller group of toys (the validation set) to see how well it has learned. Finally, it would try to sort one last group of toys it has never seen before (the test set) to show that it can recognize and sort new toys it has never seen.