Orange: Impute - Revision history

Onnowpurbo: /* Output */

2020-04-18T02:00:43Z

Output

← Older revision		Revision as of 02:00, 18 April 2020
Line 15:		Line 15:
	Beberapa algoritma dan visualisasi Orange tidak dapat menangani nilai yang tidak diketahui dalam data. Widget Impute melakukan apa yang oleh ahli statistik disebut imputasi: ia menggantikan nilai yang hilang dengan nilai yang dihitung dari data atau ditetapkan oleh pengguna. Imputasi default adalah (1-NN).		Beberapa algoritma dan visualisasi Orange tidak dapat menangani nilai yang tidak diketahui dalam data. Widget Impute melakukan apa yang oleh ahli statistik disebut imputasi: ia menggantikan nilai yang hilang dengan nilai yang dihitung dari data atau ditetapkan oleh pengguna. Imputasi default adalah (1-NN).

	[[File:Impute-stamped.png\|center\|~~200px~~\|thumb]]		[[File:Impute-stamped.png\|center\|500px\|thumb]]

Onnowpurbo at 08:19, 16 April 2020

2020-04-16T08:19:11Z

← Older revision		Revision as of 08:19, 16 April 2020
Line 2:		Line 2:


	~~Ganti~~ nilai unknown (tidak di ketahui) di data.		Widget Impute mengganti nilai unknown (tidak di ketahui) di data.

	==Input==		==Input==
Line 13:		Line 13:
	Data: dataset with imputed values		Data: dataset with imputed values

	Beberapa algoritma dan visualisasi Orange tidak dapat menangani nilai yang tidak diketahui dalam data. Widget ~~ini~~ melakukan apa yang oleh ahli statistik disebut imputasi: ia menggantikan nilai yang hilang dengan nilai yang dihitung dari data atau ditetapkan oleh pengguna. Imputasi default adalah (1-NN).		Beberapa algoritma dan visualisasi Orange tidak dapat menangani nilai yang tidak diketahui dalam data. Widget Impute melakukan apa yang oleh ahli statistik disebut imputasi: ia menggantikan nilai yang hilang dengan nilai yang dihitung dari data atau ditetapkan oleh pengguna. Imputasi default adalah (1-NN).

	[[File:Impute-stamped.png\|center\|200px\|thumb]]		[[File:Impute-stamped.png\|center\|200px\|thumb]]
Line 34:		Line 34:
	==Contoh==		==Contoh==

	Untuk mendemonstrasikan cara kerja Impute ~~widget~~, kita bermain-main dengan dataset Iris dan menghapus beberapa data. Kita menggunakan Impute ~~widget~~ dan memilih imputer berbasis Model untuk meng-impute nilai yang hilang. Di Data Table lain, kita melihat bagaimana tanda tanya berubah menjadi nilai yang berbeda ("Iris-setosa," Iris-versicolor ").		Untuk mendemonstrasikan cara kerja widget Impute, kita bermain-main dengan dataset Iris dan menghapus beberapa data. Kita menggunakan widget Impute dan memilih imputer berbasis Model untuk meng-impute nilai yang hilang. Di Data Table lain, kita melihat bagaimana tanda tanya berubah menjadi nilai yang berbeda ("Iris-setosa," Iris-versicolor ").

	[[File:Impute-Example.png\|center\|~~200px~~\|thumb]]		[[File:Impute-Example.png\|center\|600px\|thumb]]

Onnowpurbo at 09:09, 21 February 2020

2020-02-21T09:09:41Z

← Older revision		Revision as of 09:09, 21 February 2020
Line 2:		Line 2:


	~~Replaces~~ unknown ~~values in the~~ data.		Ganti nilai unknown (tidak di ketahui) di data.

	==Input==		==Input==

Onnowpurbo at 22:15, 30 January 2020

2020-01-30T22:15:25Z

← Older revision		Revision as of 22:15, 30 January 2020
Line 13:		Line 13:
	Data: dataset with imputed values		Data: dataset with imputed values

	~~Some Orange’s algorithms and visualizations cannot handle unknown values in the~~ data. ~~This widget does what statisticians call imputation~~: ~~it substitutes missing values by values either computed from the~~ data ~~or set by the user~~. ~~The~~ default ~~imputation is~~ (1-NN).		Beberapa algoritma dan visualisasi Orange tidak dapat menangani nilai yang tidak diketahui dalam data. Widget ini melakukan apa yang oleh ahli statistik disebut imputasi: ia menggantikan nilai yang hilang dengan nilai yang dihitung dari data atau ditetapkan oleh pengguna. Imputasi default adalah (1-NN).

	[[File:Impute-stamped.png\|center\|200px\|thumb]]		[[File:Impute-stamped.png\|center\|200px\|thumb]]
Line 34:		Line 34:
	==Contoh==		==Contoh==

	~~To demonstrate how the~~ Impute widget ~~works~~, ~~we played around with the~~ Iris ~~dataset and deleted some of the~~ data. ~~We used the~~ Impute widget ~~and selected the~~ Model-~~based imputer to~~ impute ~~the missing values~~. ~~In another~~ Data Table, ~~we see how the question marks turned into distinct values~~ (~~“Iris~~-setosa, ~~“Iris~~-~~versicolor”~~).		Untuk mendemonstrasikan cara kerja Impute widget, kita bermain-main dengan dataset Iris dan menghapus beberapa data. Kita menggunakan Impute widget dan memilih imputer berbasis Model untuk meng-impute nilai yang hilang. Di Data Table lain, kita melihat bagaimana tanda tanya berubah menjadi nilai yang berbeda ("Iris-setosa," Iris-versicolor ").

	[[File:Impute-Example.png\|center\|200px\|thumb]]		[[File:Impute-Example.png\|center\|200px\|thumb]]

Onnowpurbo at 05:41, 27 January 2020

2020-01-27T05:41:47Z

← Older revision		Revision as of 05:41, 27 January 2020
Line 1:		Line 1:
	Sumber: https://docs.biolab.si//3/visual-programming/widgets/data/impute.html		Sumber: https://docs.biolab.si//3/visual-programming/widgets/data/impute.html



	Replaces unknown values in the data.		Replaces unknown values in the data.

	~~Inputs~~		==Input==

	Data: input dataset		Data: input dataset
			Learner: learning algorithm for imputation

	~~Learner: learning algorithm for imputation~~		==Output==

	~~Outputs~~		Data: dataset with imputed values

	Data: dataset with imputed values

	Some Orange’s algorithms and visualizations cannot handle unknown values in the data. This widget does what statisticians call imputation: it substitutes missing values by values either computed from the data or set by the user. The default imputation is (1-NN).		Some Orange’s algorithms and visualizations cannot handle unknown values in the data. This widget does what statisticians call imputation: it substitutes missing values by values either computed from the data or set by the user. The default imputation is (1-NN).
Line 20:		Line 18:


	In the top-most box, Default method, the user can specify a general imputation technique for all attributes.		* In the top-most box, Default method, the user can specify a general imputation technique for all attributes.
			** Don’t Impute does nothing with the missing values.
	Don’t Impute does nothing with the missing values.		** Average/Most-frequent uses the average value (for continuous attributes) or the most common value (for discrete attributes).
			** As a distinct value creates new values to substitute the missing ones.
	Average/Most-frequent uses the average value (for continuous attributes) or the most common value (for discrete attributes).		** Model-based imputer constructs a model for predicting the missing value, based on values of other attributes; a separate model is constructed for each attribute. The default model is 1-NN learner, which takes the value from the most similar example (this is sometimes referred to as hot deck imputation). This algorithm can be substituted by one that the user connects to the input signal Learner for Imputation. Note, however, that if there are discrete and continuous attributes in the data, the algorithm needs to be capable of handling them both; at the moment only 1-NN learner can do that. (In the future, when Orange has more regressors, the Impute widget may have separate input signals for discrete and continuous models.)
			** Random values computes the distributions of values for each attribute and then imputes by picking random values from them.
	As a distinct value creates new values to substitute the missing ones.		** Remove examples with missing values removes the example containing missing values. This check also applies to the class attribute if Impute class values is checked.

	Model-based imputer constructs a model for predicting the missing value, based on values of other attributes; a separate model is constructed for each attribute. The default model is 1-NN learner, which takes the value from the most similar example (this is sometimes referred to as hot deck imputation). This algorithm can be substituted by one that the user connects to the input signal Learner for Imputation. Note, however, that if there are discrete and continuous attributes in the data, the algorithm needs to be capable of handling them both; at the moment only 1-NN learner can do that. (In the future, when Orange has more regressors, the Impute widget may have separate input signals for discrete and continuous models.)

	Random values computes the distributions of values for each attribute and then imputes by picking random values from them.

	Remove examples with missing values removes the example containing missing values. This check also applies to the class attribute if Impute class values is checked.

	It is possible to specify individual treatment for each attribute, which overrides the default treatment set. One can also specify a manually defined value used for imputation. In the screenshot, we decided not to impute the values of “normalized-losses” and “make”, the missing values of “aspiration” will be replaced by random values, while the missing values of “body-style” and “drive-wheels” are replaced by “hatchback” and “fwd”,respectively. If the values of “length”, “width” or “height” are missing, the example is discarded. Values of all other attributes use the default method set above (model-based imputer, in our case).

	~~The imputation methods for individual attributes are the same as default methods.~~

	~~Restore All to Default resets the individual attribute treatments to default.~~

	~~Produce a report~~.

	All changes are committed immediately if Apply automatically is checked. Otherwise, Apply needs to be ticked to apply any new settings.		* It is possible to specify individual treatment for each attribute, which overrides the default treatment set. One can also specify a manually defined value used for imputation. In the screenshot, we decided not to impute the values of “normalized-losses” and “make”, the missing values of “aspiration” will be replaced by random values, while the missing values of “body-style” and “drive-wheels” are replaced by “hatchback” and “fwd”,respectively. If the values of “length”, “width” or “height” are missing, the example is discarded. Values of all other attributes use the default method set above (model-based imputer, in our case).
			* The imputation methods for individual attributes are the same as default methods.
			* Restore All to Default resets the individual attribute treatments to default.
			* Produce a report.
			* All changes are committed immediately if Apply automatically is checked. Otherwise, Apply needs to be ticked to apply any new settings.

	==Contoh==		==Contoh==

Onnowpurbo at 07:00, 21 January 2020

2020-01-21T07:00:25Z

← Older revision		Revision as of 07:00, 21 January 2020
Line 17:		Line 17:
	Some Orange’s algorithms and visualizations cannot handle unknown values in the data. This widget does what statisticians call imputation: it substitutes missing values by values either computed from the data or set by the user. The default imputation is (1-NN).		Some Orange’s algorithms and visualizations cannot handle unknown values in the data. This widget does what statisticians call imputation: it substitutes missing values by values either computed from the data or set by the user. The default imputation is (1-NN).

	~~../../_images/impute~~-stamped.png		[[File:Impute-stamped.png\|center\|200px\|thumb]]


	In the top-most box, Default method, the user can specify a general imputation technique for all attributes.		In the top-most box, Default method, the user can specify a general imputation technique for all attributes.
Line 47:		Line 48:
	To demonstrate how the Impute widget works, we played around with the Iris dataset and deleted some of the data. We used the Impute widget and selected the Model-based imputer to impute the missing values. In another Data Table, we see how the question marks turned into distinct values (“Iris-setosa, “Iris-versicolor”).		To demonstrate how the Impute widget works, we played around with the Iris dataset and deleted some of the data. We used the Impute widget and selected the Model-based imputer to impute the missing values. In another Data Table, we see how the question marks turned into distinct values (“Iris-setosa, “Iris-versicolor”).

	~~../../_images/~~Impute-Example.png		[[File:Impute-Example.png\|center\|200px\|thumb]]

Onnowpurbo: Created page with "Sumber: https://docs.biolab.si//3/visual-programming/widgets/data/impute.html Replaces unknown values in the data. Inputs Data: input dataset Learner: learning a..."

2020-01-11T00:32:50Z

Created page with "Sumber: https://docs.biolab.si//3/visual-programming/widgets/data/impute.html Replaces unknown values in the data. Inputs Data: input dataset Learner: learning a..."

New page

Sumber: https://docs.biolab.si//3/visual-programming/widgets/data/impute.html

Replaces unknown values in the data.

Inputs

Data: input dataset

Learner: learning algorithm for imputation

Outputs

Data: dataset with imputed values

Some Orange’s algorithms and visualizations cannot handle unknown values in the data. This widget does what statisticians call imputation: it substitutes missing values by values either computed from the data or set by the user. The default imputation is (1-NN).

../../_images/impute-stamped.png

In the top-most box, Default method, the user can specify a general imputation technique for all attributes.

Don’t Impute does nothing with the missing values.

Average/Most-frequent uses the average value (for continuous attributes) or the most common value (for discrete attributes).

As a distinct value creates new values to substitute the missing ones.

Model-based imputer constructs a model for predicting the missing value, based on values of other attributes; a separate model is constructed for each attribute. The default model is 1-NN learner, which takes the value from the most similar example (this is sometimes referred to as hot deck imputation). This algorithm can be substituted by one that the user connects to the input signal Learner for Imputation. Note, however, that if there are discrete and continuous attributes in the data, the algorithm needs to be capable of handling them both; at the moment only 1-NN learner can do that. (In the future, when Orange has more regressors, the Impute widget may have separate input signals for discrete and continuous models.)

Random values computes the distributions of values for each attribute and then imputes by picking random values from them.

Remove examples with missing values removes the example containing missing values. This check also applies to the class attribute if Impute class values is checked.

It is possible to specify individual treatment for each attribute, which overrides the default treatment set. One can also specify a manually defined value used for imputation. In the screenshot, we decided not to impute the values of “normalized-losses” and “make”, the missing values of “aspiration” will be replaced by random values, while the missing values of “body-style” and “drive-wheels” are replaced by “hatchback” and “fwd”,respectively. If the values of “length”, “width” or “height” are missing, the example is discarded. Values of all other attributes use the default method set above (model-based imputer, in our case).

The imputation methods for individual attributes are the same as default methods.

Restore All to Default resets the individual attribute treatments to default.

Produce a report.

All changes are committed immediately if Apply automatically is checked. Otherwise, Apply needs to be ticked to apply any new settings.

==Contoh==

To demonstrate how the Impute widget works, we played around with the Iris dataset and deleted some of the data. We used the Impute widget and selected the Model-based imputer to impute the missing values. In another Data Table, we see how the question marks turned into distinct values (“Iris-setosa, “Iris-versicolor”).

../../_images/Impute-Example.png

==Referensi==

* https://docs.biolab.si//3/visual-programming/widgets/data/impute.html

==Pranala Menarik==

* [[Orange]]