Data matrix - Revision history

Vipul at 00:07, 28 September 2014

2014-09-28T00:07:09Z

← Older revision		Revision as of 00:07, 28 September 2014
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	==Definition==		==Definition==

	The '''data matrix''' for a machine learning problem, for a set of examples and a set of features, is defined as follows. The rows of the matrix correspond to the examples, and the columns of the matrix correspond to the features. The entry in a given row and a given column is the value of the feature for that example. In particular, if there are <math>m</math> examples and <math>n</math> features, then the data matrix is a <math>m \times n</math> matrix. In the context of [[linear regression]] and [[logistic regression]] problems, the data matrix is also termed the '''design matrix''' of the regression. ~~<math>xyzw</math>~~		The '''data matrix''' for a machine learning problem, for a set of examples and a set of features, is defined as follows. The rows of the matrix correspond to the examples, and the columns of the matrix correspond to the features. The entry in a given row and a given column is the value of the feature for that example. In particular, if there are <math>m</math> examples and <math>n</math> features, then the data matrix is a <math>m \times n</math> matrix. In the context of [[linear regression]] and [[logistic regression]] problems, the data matrix is also termed the '''design matrix''' of the regression.


	For [[supervised learning]] problems, in addition to the data matrix, a labels vector is provided that has the same dimension as <math>m</math>, the number of examples. ~~<math>uvw</math> <math>m</math>~~		For [[supervised learning]] problems, in addition to the data matrix, a labels vector is provided that has the same dimension as <math>m</math>, the number of examples.

	===Encoding unknown values===		===Encoding unknown values===

Vipul at 20:03, 15 August 2014

2014-08-15T20:03:49Z

← Older revision		Revision as of 20:03, 15 August 2014
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	==Definition==		==Definition==

	The '''data matrix''' for a machine learning problem, for a set of examples and a set of features, is defined as follows. The rows of the matrix correspond to the examples, and the columns of the matrix correspond to the features. The entry in a given row and a given column is the value of the feature for that example. In particular, if there are <math>m</math> examples and <math>n</math> features, then the data matrix is a <math>m \times n</math> matrix. In the context of [[linear regression]] and [[logistic regression]] problems, the data matrix is also termed the '''design matrix''' of the regression. <math>~~xyz~~</math>		The '''data matrix''' for a machine learning problem, for a set of examples and a set of features, is defined as follows. The rows of the matrix correspond to the examples, and the columns of the matrix correspond to the features. The entry in a given row and a given column is the value of the feature for that example. In particular, if there are <math>m</math> examples and <math>n</math> features, then the data matrix is a <math>m \times n</math> matrix. In the context of [[linear regression]] and [[logistic regression]] problems, the data matrix is also termed the '''design matrix''' of the regression. <math>xyzw</math>

Vipul: /* Definition */

2014-08-15T20:00:14Z

Definition

← Older revision		Revision as of 20:00, 15 August 2014
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	For [[supervised learning]] problems, in addition to the data matrix, a labels vector is provided that has the same dimension as <math>m</math>, the number of examples.		For [[supervised learning]] problems, in addition to the data matrix, a labels vector is provided that has the same dimension as <math>m</math>, the number of examples. <math>uvw</math> <math>m</math>

	===Encoding unknown values===		===Encoding unknown values===

Vipul at 19:55, 15 August 2014

2014-08-15T19:55:24Z

← Older revision		Revision as of 19:55, 15 August 2014
Line 1:		Line 1:
	==Definition==		==Definition==

	The '''data matrix''' for a machine learning problem, for a set of examples and a set of features, is defined as follows. The rows of the matrix correspond to the examples, and the columns of the matrix correspond to the features. The entry in a given row and a given column is the value of the feature for that example. In particular, if there are <math>m</math> examples and <math>n</math> features, then the data matrix is a <math>m \times n</math> matrix. In the context of [[linear regression]] and [[logistic regression]] problems, the data matrix is also termed the '''design matrix''' of the regression. <math>x</math>		The '''data matrix''' for a machine learning problem, for a set of examples and a set of features, is defined as follows. The rows of the matrix correspond to the examples, and the columns of the matrix correspond to the features. The entry in a given row and a given column is the value of the feature for that example. In particular, if there are <math>m</math> examples and <math>n</math> features, then the data matrix is a <math>m \times n</math> matrix. In the context of [[linear regression]] and [[logistic regression]] problems, the data matrix is also termed the '''design matrix''' of the regression. <math>xyz</math>

Vipul: /* Definition */

2014-08-15T19:37:41Z

Definition

← Older revision		Revision as of 19:37, 15 August 2014
Line 1:		Line 1:
	==Definition==		==Definition==

	The '''data matrix''' for a machine learning problem, for a set of examples and a set of features, is defined as follows. The rows of the matrix correspond to the examples, and the columns of the matrix correspond to the features. The entry in a given row and a given column is the value of the feature for that example. In particular, if there are <math>m</math> examples and <math>n</math> features, then the data matrix is a <math>m \times n</math> matrix. In the context of [[linear regression]] and [[logistic regression]] problems, the data matrix is also termed the '''design matrix''' of the regression.		The '''data matrix''' for a machine learning problem, for a set of examples and a set of features, is defined as follows. The rows of the matrix correspond to the examples, and the columns of the matrix correspond to the features. The entry in a given row and a given column is the value of the feature for that example. In particular, if there are <math>m</math> examples and <math>n</math> features, then the data matrix is a <math>m \times n</math> matrix. In the context of [[linear regression]] and [[logistic regression]] problems, the data matrix is also termed the '''design matrix''' of the regression. <math>x</math>


	For [[supervised learning]] problems, in addition to the data matrix, a labels vector is provided that has the same dimension as <math>m</math>, the number of examples.		For [[supervised learning]] problems, in addition to the data matrix, a labels vector is provided that has the same dimension as <math>m</math>, the number of examples.

Vipul: /* Definition */

2014-08-15T19:33:29Z

Definition

← Older revision		Revision as of 19:33, 15 August 2014
Line 1:		Line 1:
	==Definition==		==Definition==

	The '''data matrix''' for a machine learning problem, for a set of examples and a set of features, is defined as follows. The rows of the matrix correspond to the examples, and the columns of the matrix correspond to the features. The entry in a given row and a given column is the value of the feature for that example. In particular, if there are <math>m</math> examples and <math>n</math> features, then the data matrix is a <math>m \times n</math> matrix. In the context of [[linear regression]] and [[logistic regression]] problems, the data matrix is also termed the '''design matrix''' of the regression. ~~<math>x</math>~~		The '''data matrix''' for a machine learning problem, for a set of examples and a set of features, is defined as follows. The rows of the matrix correspond to the examples, and the columns of the matrix correspond to the features. The entry in a given row and a given column is the value of the feature for that example. In particular, if there are <math>m</math> examples and <math>n</math> features, then the data matrix is a <math>m \times n</math> matrix. In the context of [[linear regression]] and [[logistic regression]] problems, the data matrix is also termed the '''design matrix''' of the regression.

	For [[supervised learning]] problems, in addition to the data matrix, a labels vector is provided that has the same dimension as <math>m</math>, the number of examples.		For [[supervised learning]] problems, in addition to the data matrix, a labels vector is provided that has the same dimension as <math>m</math>, the number of examples.

Vipul: /* Definition */

2014-08-15T19:32:45Z

Definition

← Older revision		Revision as of 19:32, 15 August 2014
Line 1:		Line 1:
	==Definition==		==Definition==

	The '''data matrix''' for a machine learning problem, for a set of examples and a set of features, is defined as follows. The rows of the matrix correspond to the examples, and the columns of the matrix correspond to the features. The entry in a given row and a given column is the value of the feature for that example. In particular, if there are <math>m</math> examples and <math>n</math> features, then the data matrix is a <math>m \times n</math> matrix. In the context of [[linear regression]] and [[logistic regression]] problems, the data matrix is also termed the '''design matrix''' of the regression.		The '''data matrix''' for a machine learning problem, for a set of examples and a set of features, is defined as follows. The rows of the matrix correspond to the examples, and the columns of the matrix correspond to the features. The entry in a given row and a given column is the value of the feature for that example. In particular, if there are <math>m</math> examples and <math>n</math> features, then the data matrix is a <math>m \times n</math> matrix. In the context of [[linear regression]] and [[logistic regression]] problems, the data matrix is also termed the '''design matrix''' of the regression. <math>x</math>

	For [[supervised learning]] problems, in addition to the data matrix, a labels vector is provided that has the same dimension as <math>m</math>, the number of examples.		For [[supervised learning]] problems, in addition to the data matrix, a labels vector is provided that has the same dimension as <math>m</math>, the number of examples.

Vipul: /* Definition */

2014-08-15T19:31:50Z

Definition

← Older revision		Revision as of 19:31, 15 August 2014
Line 1:		Line 1:
	==Definition==		==Definition==

	The '''data matrix''' for a machine learning problem, for a set of examples and a set of features, is defined as follows. The rows of the matrix correspond to the examples, and the columns of the matrix correspond to the features. The entry in a given row and a given column is the value of the feature for that example. In particular, if there are <math>m</math> examples and <math>n</math> features, then the data matrix is a <math>m \times n</math> matrix. In the context of [[linear regression]] and [[logistic regression]] problems, the data matrix is also termed the '''design matrix''' of the regression. ~~<math>x</math>~~		The '''data matrix''' for a machine learning problem, for a set of examples and a set of features, is defined as follows. The rows of the matrix correspond to the examples, and the columns of the matrix correspond to the features. The entry in a given row and a given column is the value of the feature for that example. In particular, if there are <math>m</math> examples and <math>n</math> features, then the data matrix is a <math>m \times n</math> matrix. In the context of [[linear regression]] and [[logistic regression]] problems, the data matrix is also termed the '''design matrix''' of the regression.

	For [[supervised learning]] problems, in addition to the data matrix, a labels vector is provided that has the same dimension as <math>m</math>, the number of examples.		For [[supervised learning]] problems, in addition to the data matrix, a labels vector is provided that has the same dimension as <math>m</math>, the number of examples.

Vipul: /* Definition */

2014-08-15T19:31:40Z

Definition

← Older revision		Revision as of 19:31, 15 August 2014
Line 1:		Line 1:
	==Definition==		==Definition==

	The '''data matrix''' for a machine learning problem, for a set of examples and a set of features, is defined as follows. The rows of the matrix correspond to the examples, and the columns of the matrix correspond to the features. The entry in a given row and a given column is the value of the feature for that example. In particular, if there are <math>m</math> examples and <math>n</math> features, then the data matrix is a <math>m \times n</math> matrix. In the context of [[linear regression]] and [[logistic regression]] problems, the data matrix is also termed the '''design matrix''' of the regression.		The '''data matrix''' for a machine learning problem, for a set of examples and a set of features, is defined as follows. The rows of the matrix correspond to the examples, and the columns of the matrix correspond to the features. The entry in a given row and a given column is the value of the feature for that example. In particular, if there are <math>m</math> examples and <math>n</math> features, then the data matrix is a <math>m \times n</math> matrix. In the context of [[linear regression]] and [[logistic regression]] problems, the data matrix is also termed the '''design matrix''' of the regression. <math>x</math>

	For [[supervised learning]] problems, in addition to the data matrix, a labels vector is provided that has the same dimension as <math>m</math>, the number of examples.		For [[supervised learning]] problems, in addition to the data matrix, a labels vector is provided that has the same dimension as <math>m</math>, the number of examples.

Vipul: /* Definition */

2014-08-15T19:31:17Z

Definition

← Older revision		Revision as of 19:31, 15 August 2014
Line 1:		Line 1:
	==Definition==		==Definition==

	The '''data matrix''' for a machine learning problem, for a set of examples and a set of features, is defined as follows. The rows of the matrix correspond to the examples, and the columns of the matrix correspond to the features. The entry in a given row and a given column is the value of the feature for that example. In particular, if there are <math>m</math> examples and <math>n</math> features, then the data matrix is a <math>m \times n</math> matrix. In the context of [[linear regression]] and [[logistic regression]] problems, the data matrix is also termed the '''design matrix''' of the regression. ~~<math>x</math>~~		The '''data matrix''' for a machine learning problem, for a set of examples and a set of features, is defined as follows. The rows of the matrix correspond to the examples, and the columns of the matrix correspond to the features. The entry in a given row and a given column is the value of the feature for that example. In particular, if there are <math>m</math> examples and <math>n</math> features, then the data matrix is a <math>m \times n</math> matrix. In the context of [[linear regression]] and [[logistic regression]] problems, the data matrix is also termed the '''design matrix''' of the regression.

	For [[supervised learning]] problems, in addition to the data matrix, a labels vector is provided that has the same dimension as <math>m</math>, the number of examples.		For [[supervised learning]] problems, in addition to the data matrix, a labels vector is provided that has the same dimension as <math>m</math>, the number of examples.