Feature vector

See also: Machine learning terms

=Introduction

Machine learning utilizes feature vectors, which are numerical values that describe an object or phenomenon. A feature vector may be defined as an n-dimensional array of numerical features representing a data point or example.

As an array of feature values that represent an example, feature vector is used in training the model and using the model to make predictions (inference).

Creating Feature Vectors

Feature vectors are created by extracting relevant features from raw data. Feature extraction involves selecting the most significant characteristics and representing them numerically. For instance, in image recognition, relevant features might include color, texture, and shape of an object in an image; similarly in natural language processing these might include word or phrase frequencies within a document.

Once features have been identified, they are transformed into a vector of numerical values known as the feature vector. This feature vector can then be fed into various machine learning algorithms for input. Constructing such a feature vector often involves complex techniques like dimensionality reduction or feature scaling that require specialized expertise.

Why are Feature Vectors Important?

Feature vectors are essential in representing complex data in an easily comprehendible form. Machine learning algorithms use feature vectors to quickly compare and manipulate data points, making it possible to perform various tasks such as classification, regression, and clustering more effectively.

Another noteworthy characteristic of feature vectors is their potential to be exploited through powerful mathematical techniques like linear algebra and calculus. These can be employed to transform and manipulate feature vectors in order to reveal hidden patterns and relationships within data, leading to new insights and the development of more precise machine learning models.

Explain Like I'm 5 (ELI5)

Machine learning is the process by which computers learn how to do certain things for themselves. For instance, we can teach a computer to recognize different animals in pictures by instructing it what features to look for - such as pointy ears and tails - on each image. With this approach, the computer learns to do things on its own.

Once we have identified the important features, we convert them into a list of numbers the computer can utilize - this is known as a "feature vector". The computer uses this feature vector to determine what kind of animal is in a picture. We can use feature vectors for many other things too, like recommending movies or predicting weather forecasts. Feature vectors provide easy-to-understand understanding to the computer so it can make decisions more efficiently.

Explain Like I'm 5 (ELI5)

Imagine you have a collection of pictures featuring different fruits like apples, bananas and oranges. In order to teach a computer how to recognize these items, you would need to provide it with some information about each fruit - such as its color, size and shape.

A feature vector is like a comprehensive list of all the pertinent details you want your computer to know about each fruit, such as its shape. For instance, an apple's feature vector might look something like this:

[Rougeness, Roundness, Size and Sweetness]

Each item in the list identifies an apple by computer using its unique feature vector. Comparing apples to bananas or oranges, their feature vectors would differ since those fruits possess distinct traits.

By providing the computer with multiple feature vectors for different fruits, it can learn to distinguish each fruit based on its distinctive set of traits.