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Feature vector: Difference between revisions
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(Created page with "{{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 (inferen...") |
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{{see also|Machine learning terms}} | {{see also|Machine learning terms}} | ||
==Introduction== | |||
[[Machine learning]] utilizes [[feature vector]]s, 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]]. | [[Machine learning]] utilizes [[feature vector]]s, 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]]. | ||
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==Creating Feature Vectors== | ==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. | 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 | 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 [[algorithm]]s for [[input]]. Constructing such a feature vector often involves complex techniques like [[dimensionality reduction]] or [[feature scaling]] that require specialized expertise. | ||
==Example== | |||
Let us assume we have a [[dataset]] of images of handwritten digits (0-9), and want to use machine learning algorithms to [[classify]] these images into their corresponding digits. In order to do this, we need to extract features from the images which can be utilized as features vectors for use by machine learning algorithms; one way of doing this is by converting the images into feature vectors. | |||
Take for instance this 28x28 grayscale image of the numeral "5": | |||
[Image of the numeral "5"] | |||
From this image we can deduce the following features: | |||
*Mean intensity of pixels | |||
*Standard deviation of pixel intensities | |||
*Skewness of pixel intensities | |||
*Kurtosis of pixel intensities | |||
Our feature vector for this image would then be: | |||
[Mean intensity, Standard deviation, Skewness and Kurtosis] = [123, 10, 0.5, 2.0] | |||
This feature vector, consisting of 4 features, represents the image of digit "5". Machine learning algorithms can use this feature vector as input and train themselves to classify images based on these features. | |||
==Why are Feature Vectors Important?== | ==Why are Feature Vectors Important?== | ||