Data representation machine learning

What is data representation in ML?
A machine learning model can't directly see, hear, or sense input examples.
Instead, you must create a representation of the data to provide the model with a useful vantage point into the data's key qualities.
That is, in order to train a model, you must choose the set of features that best represent the data._{Jul 14, 2023}.
What type of data is used for machine learning?
Labeled data includes a label or target variable that the model is trying to predict, whereas unlabeled data does not include a label or target variable.
The data used in machine learning is typically numerical or categorical.
Numerical data includes values that can be ordered and measured, such as age or income..
Representation learning refers to the process of learning a representation yi=f(xi) from an input object xi toward a specific task, for example, classification, retrieval, clustering, and others.
From: Deep Learning for Robot Perception and Cognition, 2022.

Why do machine learning algorithms have different representations?

Representation Learning: A Review and New Perspectives The success of machine learning algorithms generally depends on data representation, and we hypothesize that this is because different representations can entangle and hide more or less the different ex- planatory factors of variation behind the data

In machine learning, feature learning or representation learning is a set of techniques that allows a system to automatically discover the representations needed for feature detection or classification from raw data.

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Data representation machine learning

What is data representation in ML?

What type of data is used for machine learning?

Why do machine learning algorithms have different representations?