Input

Definition of Input

Input: Input refers to the data that is given to a machine learning algorithm in order to learn from it. The input can be in a number of formats, including text, images, and videos.

What are the most common types of Input given to machine learning algorithms?

The most common types of inputs given to machine learning algorithms are structured data, unstructured data, and labeled data. Structured data is essentially any type of information that has been organized in some way. Examples of structured data can include numerical values, dates, times, payment information, customer surveys and more. Unstructured data is information that does not have a predefined structure or format. Examples of unstructured data can be images, audio recordings, video clips and text documents. Labeled data refers to the type of input that contains both input features (data) as well as labels for each instance in the dataset. This allows the algorithm to use the labeled dataset to learn how to identify instances based on its associated labels.

When it comes to training machine learning models, sometimes using unlabeled datasets can also yield good results since it eliminates the need for manual labeling which can be time consuming and expensive. However, when working with supervised learning tasks such as object recognition or classification tasks it is usually necessary to provide labeled datasets so that the algorithm can learn from its associated labels. It should also be noted that feature engineering plays an important role in providing higher quality inputs for machine learning algorithms so understanding which types of features are appropriate for a particular task is essential in achieving good performance from a trained model.