Orange: Randomize - Revision history

Onnowpurbo: /* Contoh */

2020-04-04T06:16:16Z

Contoh

← Older revision		Revision as of 06:16, 4 April 2020
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	Widget Randomize biasanya ditempatkan tepat setelah input data, misalnya widget File. Penggunaan dasar ditunjukkan dalam workflow berikut, di mana nilai-nilai variabel class dataset Iris secara acak dikocok.		Widget Randomize biasanya ditempatkan tepat setelah input data, misalnya widget File. Penggunaan dasar ditunjukkan dalam workflow berikut, di mana nilai-nilai variabel class dataset Iris secara acak dikocok.

	[[File:Randomize-Example1.png\|center\|~~200px~~\|thumb]]		[[File:Randomize-Example1.png\|center\|600px\|thumb]]

	Dalam contoh berikut, diperlihatkan bahwa pengocokan nilai class mempengaruhi kinerja model. Walaupun menggunakan dataset yang sama, kinerja learner akan berbeda untuk data yang tidak melalui widget Randomize dan yang melalui widget Randomize.		Dalam contoh berikut, diperlihatkan bahwa pengocokan nilai class mempengaruhi kinerja model. Walaupun menggunakan dataset yang sama, kinerja learner akan berbeda untuk data yang tidak melalui widget Randomize dan yang melalui widget Randomize.

	[[File:Randomize-Example2.png\|center\|~~200px~~\|thumb]]		[[File:Randomize-Example2.png\|center\|600px\|thumb]]

	==Referensi==		==Referensi==

Onnowpurbo: /* Contoh */

2020-04-04T06:15:40Z

Contoh

← Older revision		Revision as of 06:15, 4 April 2020
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	==Contoh==		==Contoh==

	Widget Randomize biasanya ditempatkan tepat ~~setelahnya (mis.~~ widget File). Penggunaan dasar ditunjukkan dalam workflow berikut, di mana nilai-nilai variabel class dataset Iris secara acak dikocok.		Widget Randomize biasanya ditempatkan tepat setelah input data, misalnya widget File. Penggunaan dasar ditunjukkan dalam workflow berikut, di mana nilai-nilai variabel class dataset Iris secara acak dikocok.

	[[File:Randomize-Example1.png\|center\|200px\|thumb]]		[[File:Randomize-Example1.png\|center\|200px\|thumb]]

Onnowpurbo: /* Output */

2020-04-04T06:13:49Z

Output

← Older revision		Revision as of 06:13, 4 April 2020
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	Data: randomized dataset		Data: randomized dataset

	Widget Randomize menerima dataset pada input dan akan mengeluarkan dataset yang sama di mana ~~kelas~~, atribut atau / dan meta diacak.		Widget Randomize menerima dataset pada input dan akan mengeluarkan dataset yang sama di mana class, atribut atau / dan meta diacak.

	[[File:Randomize-Default.png\|center\|200px\|thumb]]		[[File:Randomize-Default.png\|center\|200px\|thumb]]

Onnowpurbo: /* Contoh */

2020-04-04T06:09:45Z

Contoh

← Older revision		Revision as of 06:09, 4 April 2020
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	[[File:Randomize-Example1.png\|center\|200px\|thumb]]		[[File:Randomize-Example1.png\|center\|200px\|thumb]]

	Dalam contoh berikut, ~~kita menunjukkan bagaimana~~ pengocokan nilai class mempengaruhi kinerja model ~~pada~~ dataset yang sama ~~seperti di atas~~.		Dalam contoh berikut, diperlihatkan bahwa pengocokan nilai class mempengaruhi kinerja model. Walaupun menggunakan dataset yang sama, kinerja learner akan berbeda untuk data yang tidak melalui widget Randomize dan yang melalui widget Randomize.

	[[File:Randomize-Example2.png\|center\|200px\|thumb]]		[[File:Randomize-Example2.png\|center\|200px\|thumb]]




	==Referensi==		==Referensi==

Onnowpurbo: /* Output */

2020-04-04T06:07:52Z

Output

← Older revision		Revision as of 06:07, 4 April 2020
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	Data: randomized dataset		Data: randomized dataset

	Widget Randomize menerima dataset pada input dan akan ~~meng-output~~ dataset yang sama di mana kelas, atribut atau / dan meta diacak.		Widget Randomize menerima dataset pada input dan akan mengeluarkan dataset yang sama di mana kelas, atribut atau / dan meta diacak.

	[[File:Randomize-Default.png\|center\|200px\|thumb]]		[[File:Randomize-Default.png\|center\|200px\|thumb]]

Onnowpurbo at 05:10, 4 April 2020

2020-04-04T05:10:36Z

← Older revision		Revision as of 05:10, 4 April 2020
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	~~Mengacak~~ class, attribute dan/atau meta dari input dataset.		Widget Randomize mengacak class, attribute dan/atau meta dari input dataset.

	==Input==		==Input==
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	Data: randomized dataset		Data: randomized dataset

	Randomize ~~widget~~ menerima dataset pada input dan akan meng-output dataset yang sama di mana kelas, atribut atau / dan meta diacak.		Widget Randomize menerima dataset pada input dan akan meng-output dataset yang sama di mana kelas, atribut atau / dan meta diacak.

	[[File:Randomize-Default.png\|center\|200px\|thumb]]		[[File:Randomize-Default.png\|center\|200px\|thumb]]
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	==Contoh==		==Contoh==

	Randomize ~~widget~~ biasanya ditempatkan tepat setelahnya (mis. File ~~widget~~). Penggunaan dasar ditunjukkan dalam workflow berikut, di mana nilai-nilai variabel class dataset Iris secara acak dikocok.		Widget Randomize biasanya ditempatkan tepat setelahnya (mis. widget File). Penggunaan dasar ditunjukkan dalam workflow berikut, di mana nilai-nilai variabel class dataset Iris secara acak dikocok.

	[[File:Randomize-Example1.png\|center\|200px\|thumb]]		[[File:Randomize-Example1.png\|center\|200px\|thumb]]

Onnowpurbo at 04:59, 27 January 2020

2020-01-27T04:59:33Z

← Older revision		Revision as of 04:59, 27 January 2020
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	[[File:Randomize-Default.png\|center\|200px\|thumb]]		[[File:Randomize-Default.png\|center\|200px\|thumb]]

	* ~~Select group of columns of the~~ dataset ~~you want to shuffle~~.		* Pilih grup kolom dataset yang ingin kita kocok.
	* ~~Select proportion of the~~ dataset ~~you want to shuffle~~.		* Pilih proporsi dataset yang ingin kita kocok.
	* ~~Produce replicable~~ output.		* Hasilkan output yang dapat di replikasi.
	* If Apply automatically ~~is ticked~~, ~~changes are committed automatically~~. ~~Otherwise~~, ~~you have to press~~ Apply ~~after each change~~.		* Jika Apply automatically dicentang, perubahan dilakukan secara otomatis. Jika tidak, kita harus menekan Apply setelah setiap perubahan.
	* Hasilkan Report.		* Hasilkan Report.

Onnowpurbo at 04:50, 27 January 2020

2020-01-27T04:50:57Z

← Older revision		Revision as of 04:50, 27 January 2020
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	~~Shuffles classes~~, ~~attributes and~~/~~or metas of an~~ input dataset.		Mengacak class, attribute dan/atau meta dari input dataset.

	==Input==		==Input==
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	Data: randomized dataset		Data: randomized dataset

	~~The~~ Randomize widget ~~receives a~~ dataset ~~in the~~ input ~~and outputs the same~~ dataset ~~in which the classes~~, ~~attributes or~~/~~and metas are shuffled~~.		Randomize widget menerima dataset pada input dan akan meng-output dataset yang sama di mana kelas, atribut atau / dan meta diacak.

	[[File:Randomize-Default.png\|center\|200px\|thumb]]		[[File:Randomize-Default.png\|center\|200px\|thumb]]

			* Select group of columns of the dataset you want to shuffle.
	Select group of columns of the dataset you want to shuffle.		* Select proportion of the dataset you want to shuffle.
	Select proportion of the dataset you want to shuffle.		* Produce replicable output.
	Produce replicable output.		* If Apply automatically is ticked, changes are committed automatically. Otherwise, you have to press Apply after each change.
	If Apply automatically is ticked, changes are committed automatically. Otherwise, you have to press Apply after each change.		* Hasilkan Report.
	~~Produce a report~~.

	==Contoh==		==Contoh==

	~~The~~ Randomize widget ~~is usually placed right after~~ (~~e.g~~. File widget. ~~The basic usage is shown in the following~~ workflow, ~~where values of~~ class ~~variable of~~ Iris ~~dataset are randomly shuffled~~.		Randomize widget biasanya ditempatkan tepat setelahnya (mis. File widget). Penggunaan dasar ditunjukkan dalam workflow berikut, di mana nilai-nilai variabel class dataset Iris secara acak dikocok.

	[[File:Randomize-Example1.png\|center\|200px\|thumb]]		[[File:Randomize-Example1.png\|center\|200px\|thumb]]

	~~In the next example we show how shuffling~~ class ~~values influences~~ model ~~performance on the same~~ dataset ~~as above~~.		Dalam contoh berikut, kita menunjukkan bagaimana pengocokan nilai class mempengaruhi kinerja model pada dataset yang sama seperti di atas.

	[[File:Randomize-Example2.png\|center\|200px\|thumb]]		[[File:Randomize-Example2.png\|center\|200px\|thumb]]

Onnowpurbo at 04:30, 27 January 2020

2020-01-27T04:30:06Z

← Older revision		Revision as of 04:30, 27 January 2020
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	Shuffles classes, attributes and/or metas of an input dataset.		Shuffles classes, attributes and/or metas of an input dataset.

	~~Inputs~~		==Input==

	Data: input dataset		Data: input dataset

	~~Outputs~~		==Output==

	Data: randomized dataset		Data: randomized dataset

	The Randomize widget receives a dataset in the input and outputs the same dataset in which the classes, attributes or/and metas are shuffled.		The Randomize widget receives a dataset in the input and outputs the same dataset in which the classes, attributes or/and metas are shuffled.
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	Select group of columns of the dataset you want to shuffle.		Select group of columns of the dataset you want to shuffle.

	Select proportion of the dataset you want to shuffle.		Select proportion of the dataset you want to shuffle.

	Produce replicable output.		Produce replicable output.

	If Apply automatically is ticked, changes are committed automatically. Otherwise, you have to press Apply after each change.		If Apply automatically is ticked, changes are committed automatically. Otherwise, you have to press Apply after each change.

	Produce a report.		Produce a report.

Onnowpurbo at 04:29, 21 January 2020

2020-01-21T04:29:36Z

← Older revision		Revision as of 04:29, 21 January 2020
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	The Randomize widget receives a dataset in the input and outputs the same dataset in which the classes, attributes or/and metas are shuffled.		The Randomize widget receives a dataset in the input and outputs the same dataset in which the classes, attributes or/and metas are shuffled.

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


	Select group of columns of the dataset you want to shuffle.		Select group of columns of the dataset you want to shuffle.
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	Produce a report.		Produce a report.

	~~Example~~		==Contoh==

	The Randomize widget is usually placed right after (e.g. File widget. The basic usage is shown in the following workflow, where values of class variable of Iris dataset are randomly shuffled.		The Randomize widget is usually placed right after (e.g. File widget. The basic usage is shown in the following workflow, where values of class variable of Iris dataset are randomly shuffled.

	~~../../_images/~~Randomize-Example1.png		[[File:Randomize-Example1.png\|center\|200px\|thumb]]

	In the next example we show how shuffling class values influences model performance on the same dataset as above.		In the next example we show how shuffling class values influences model performance on the same dataset as above.

	~~../../_images/~~Randomize-Example2.png		[[File:Randomize-Example2.png\|center\|200px\|thumb]]