Sample data

Sample data (also known as training data, learning data or training set) is an important part of machine learning (ML).

What is sample data?

Sample data plays an important role in Machine learning and are used to train and improve ML algorithms so that they can learn from data and make predictions or make decisions. The Quality and representativeness of sample data have a major impact on the algorithm's learning performance.

What are the properties of sample data?

Representativeness: The sample data should be the Target population represent well, from which the algorithm should make predictions later.
Quality: The sample data should accurate, entirely and consistent be. Incorrect or incomplete data can lead to incorrect results.
Size: The amount of sample data has an influence on the learning performance of the algorithm. The more data the algorithm has at its disposal, the better it can learn.
Formato: The sample data must be in a format that can be processed by the ML algorithm. This can be, for example, a table format, a text format or a image format be.

What types of sample data are there?

structured data: structured data are available in a tabular format, with each column representing a specific property or variable.
Unstructured data: Unstructured data are available in a free format, e.g. in the form of texts, images, or videos.
Semi-structured data: Semi-structured data contain both structured and unstructured elements.

What is there to consider when creating sample data?

Sample data can be created in a variety of ways:

Manual data collection: The sample data is collected manually by people, for example through surveys or observations.
Data collection from existing sources: The sample data is obtained from existing sources such as databases or extracts sensors.
Data generation: The sample data is generated synthetically, e.g. using simulation models.

How is sample data preprocessed?

Before the sample data for the Training an ML algorithm can be used, they must be used often preprocessed become.

This includes steps such as:

Data cleansing: Inaccurate or incomplete data will be removed or corrected.
Normalization: The data is brought to a uniform scale.
Feature engineering: New features are derived from existing data.

How do you save sample data?

Sample data should be on a safe place that is protected from unauthorized access.

It is also important that provenance and the workmanship to document the sample data.

Note: Our team benefited from the support of AI technologies while creating and maintaining this glossary.

Sample data is the training partners for ML.

Constant learning is the goal.

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