Vipul: /* The distinct between elementary and derived features */

2014-06-25T17:07:25Z

The distinct between elementary and derived features

← Older revision		Revision as of 17:07, 25 June 2014
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	In some cases, there are ''probabilistic'' dependencies between the features: knowledge of the values of some of the features affects the probability distribution of the values of other features, even though all values are still possible.		In some cases, there are ''probabilistic'' dependencies between the features: knowledge of the values of some of the features affects the probability distribution of the values of other features, even though all values are still possible.

	===The ~~distinct~~ between elementary and derived features===		===The distinction between elementary and derived features===

	One way of conceptualizing dependencies between features is to distinguish between ''elementary'' features and ''derived'' features. The set of elementary features is generally a set of (almost) independent features. Derived features are obtained through mathematical functions of the elementary features. For instance, if we have three elementary features <math>x_1,x_2,x_3</math>, we can construct various derived features from them, such as <math>x_1^2,x_1x_2,x_2^2</math>.		One way of conceptualizing dependencies between features is to distinguish between ''elementary'' features and ''derived'' features. The set of elementary features is generally a set of (almost) independent features. Derived features are obtained through mathematical functions of the elementary features. For instance, if we have three elementary features <math>x_1,x_2,x_3</math>, we can construct various derived features from them, such as <math>x_1^2,x_1x_2,x_2^2</math>.

	The choice of what derived features to use can be viewed as part of either [[feature selection]] or [[model selection]], because the derived features could be viewed either as features or as ways of using the features in a model to predict the output.		The choice of what derived features to use can be viewed as part of either [[feature selection]] or [[model selection]], because the derived features could be viewed either as features or as ways of using the features in a model to predict the output.

Vipul: Created page with "==Definition== In machine learning problems, the term '''feature''' is used for an input that is used to predict the output of a function. For instance, if we are predicting..."

2014-06-18T20:43:59Z

Created page with "==Definition== In machine learning problems, the term '''feature''' is used for an input that is used to predict the output of a function. For instance, if we are predicting..."

New page

==Definition==

In machine learning problems, the term '''feature''' is used for an input that is used to predict the output of a function. For instance, if we are predicting the price of a house, one of the features may be the total area of the house: knowledge of this feature can help with predicting the house price.

==Feature selection==

{{further|[[feature selection]]}}

==Concepts related to features==

===Dependencies between features===

A set of features may be completely independent, or there may be partial dependencies between the features. For instance, consider the problem of determining the price of a house of a rectangular shape. Features of the house that determine the price may include the length, breadth, and area of the house. The features length, breadth, and area, are related to each other: the area is the product of the length and breadth. In general, if the values for some feature determine or constrain the values for other features, then the features are dependent.

In some cases, there are ''probabilistic'' dependencies between the features: knowledge of the values of some of the features affects the probability distribution of the values of other features, even though all values are still possible.

===The distinct between elementary and derived features===

One way of conceptualizing dependencies between features is to distinguish between ''elementary'' features and ''derived'' features. The set of elementary features is generally a set of (almost) independent features. Derived features are obtained through mathematical functions of the elementary features. For instance, if we have three elementary features <math>x_1,x_2,x_3</math>, we can construct various derived features from them, such as <math>x_1^2,x_1x_2,x_2^2</math>.

The choice of what derived features to use can be viewed as part of either [[feature selection]] or [[model selection]], because the derived features could be viewed either as features or as ways of using the features in a model to predict the output.

Feature - Revision history

Vipul: /* The distinct between elementary and derived features */

Vipul: Created page with "==Definition== In machine learning problems, the term '''feature''' is used for an input that is used to predict the output of a function. For instance, if we are predicting..."