Orange: Distributions - Revision history

Onnowpurbo at 02:35, 4 March 2020

2020-03-04T02:35:39Z

← Older revision		Revision as of 02:35, 4 March 2020
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	Untuk atribut kontinu, nilai atribut ditampilkan sebagai grafik fungsi. Probabilitas class untuk atribut kontinu diperoleh dengan estimasi kepadatan kernel Gaussian, sedangkan tampilan kurva diatur dengan Precision bar (smooth atau precise).		Untuk atribut kontinu, nilai atribut ditampilkan sebagai grafik fungsi. Probabilitas class untuk atribut kontinu diperoleh dengan estimasi kepadatan kernel Gaussian, sedangkan tampilan kurva diatur dengan Precision bar (smooth atau precise).

	~~For the purpose of this example~~, ~~we used the~~ Iris ~~dataset~~.		Sebagai contoh, di bawah ini kita menggunakan dataset Iris.

	[[File:Distributions-Cont.png\|center\|200px\|thumb]]		[[File:Distributions-Cont.png\|center\|200px\|thumb]]

Onnowpurbo: /* Input */

2020-03-04T02:34:23Z

Input

← Older revision		Revision as of 02:34, 4 March 2020
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	Data: input dataset		Data: input dataset

	Widget ~~Distribusi~~ menampilkan distribusi nilai atribut diskrit atau kontinu. Jika data berisi variabel class, distribusi dapat dikondisikan di class.		Widget Distributions menampilkan distribusi nilai atribut diskrit atau kontinu. Jika data berisi variabel class, distribusi dapat dikondisikan di class.

	Untuk atribut diskrit, grafik yang ditampilkan oleh widget menunjukkan berapa kali (mis., berapa banyak instance) setiap nilai atribut muncul dalam data. Jika data berisi variabel class, distribusi class untuk setiap nilai atribut akan ditampilkan juga (seperti dalam snapshot di bawah). Untuk membuat grafik ini, kita menggunakan dataset Zoo.		Untuk atribut diskrit, grafik yang ditampilkan oleh widget menunjukkan berapa kali (mis., berapa banyak instance) setiap nilai atribut muncul dalam data. Jika data berisi variabel class, distribusi class untuk setiap nilai atribut akan ditampilkan juga (seperti dalam snapshot di bawah). Untuk membuat grafik ini, kita menggunakan dataset Zoo.
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	[[File:Distributions-NoClass.png\|center\|200px\|thumb]]		[[File:Distributions-NoClass.png\|center\|200px\|thumb]]


	==Referensi==		==Referensi==

Onnowpurbo at 01:01, 3 February 2020

2020-02-03T01:01:50Z

← Older revision		Revision as of 01:01, 3 February 2020
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	~~Displays value distributions for a single attribute~~.		Menampilkan distribusi nilai untuk satu atribut.

	==Input==		==Input==
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	Data: input dataset		Data: input dataset

	~~The Distributions widget displays the value distribution of discrete or continuous attributes~~. ~~If the~~ data ~~contains a~~ class ~~variable~~, ~~distributions may be conditioned on the~~ class.		Widget Distribusi menampilkan distribusi nilai atribut diskrit atau kontinu. Jika data berisi variabel class, distribusi dapat dikondisikan di class.

	~~For discrete attributes~~, ~~the graph displayed by the~~ widget ~~shows how many times~~ (~~e.g~~., ~~in how many instances~~) ~~each attribute value appears in the~~ data. ~~If the~~ data ~~contains a~~ class ~~variable~~, class ~~distributions for each of the attribute values will be displayed as well~~ (~~like in the~~ snapshot ~~below~~). ~~In order to create this graph~~, ~~we used the~~ Zoo ~~dataset~~.		Untuk atribut diskrit, grafik yang ditampilkan oleh widget menunjukkan berapa kali (mis., berapa banyak instance) setiap nilai atribut muncul dalam data. Jika data berisi variabel class, distribusi class untuk setiap nilai atribut akan ditampilkan juga (seperti dalam snapshot di bawah). Untuk membuat grafik ini, kita menggunakan dataset Zoo.

	[[File:Distributions-Disc-stamped.png\|center\|200px\|thumb]]		[[File:Distributions-Disc-stamped.png\|center\|200px\|thumb]]
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	* Produce a report.		* Produce a report.

	~~For continuous attributes~~, ~~the attribute values are displayed as a function graph~~. ~~Class probabilities for continuous attributes are obtained with~~ Gaussian ~~kernel density estimation~~, ~~while the appearance of the curve is set with the~~ Precision bar (smooth or precise).		Untuk atribut kontinu, nilai atribut ditampilkan sebagai grafik fungsi. Probabilitas class untuk atribut kontinu diperoleh dengan estimasi kepadatan kernel Gaussian, sedangkan tampilan kurva diatur dengan Precision bar (smooth atau precise).

	For the purpose of this example, we used the Iris dataset.		For the purpose of this example, we used the Iris dataset.
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	[[File:Distributions-Cont.png\|center\|200px\|thumb]]		[[File:Distributions-Cont.png\|center\|200px\|thumb]]

	In class-less ~~domains~~, ~~the bars are displayed in gray~~. ~~Here we~~ set ~~Bin continuous variables into~~ 10 ~~bins~~, ~~which distributes variables into~~ 10 ~~intervals and displays averages of these intervals as histograms~~ (~~see~~ 2. ~~above~~). ~~We used the~~ Housing ~~dataset~~.		Dalam class-less domain, bar yang ditampilkan berwarna abu-abu. Di sini kita set variabel kontinu menjadi 10 bin, yang mendistribusikan variabel ke dalam 10 interval dan menampilkan rata-rata interval ini sebagai histogram (lihat 2. di atas). Kita menggunakan dataset Housing.

	[[File:Distributions-NoClass.png\|center\|200px\|thumb]]		[[File:Distributions-NoClass.png\|center\|200px\|thumb]]

Onnowpurbo at 01:29, 28 January 2020

2020-01-28T01:29:06Z

← Older revision		Revision as of 01:29, 28 January 2020
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	Displays value distributions for a single attribute.		Displays value distributions for a single attribute.

	~~Inputs~~		==Input==

	Data: input dataset		Data: input dataset

	The Distributions widget displays the value distribution of discrete or continuous attributes. If the data contains a class variable, distributions may be conditioned on the class.		The Distributions widget displays the value distribution of discrete or continuous attributes. If the data contains a class variable, distributions may be conditioned on the class.
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	[[File:Distributions-Disc-stamped.png\|center\|200px\|thumb]]		[[File:Distributions-Disc-stamped.png\|center\|200px\|thumb]]

	A list of variables for distributions display		* A list of variables for distributions display
			* If Bin continuous variables is ticked, the widget will discretize continuous variables by assigning them to intervals. The number of intervals is set by precision scale. Alternatively, you can set smoothness for the distribution curves of continuous variables.
	If Bin continuous variables is ticked, the widget will discretize continuous variables by assigning them to intervals. The number of intervals is set by precision scale. Alternatively, you can set smoothness for the distribution curves of continuous variables.		* The widget may be requested to display value distributions only for instances of certain class (Group by). Show relative frequencies will scale the data by percentage of the dataset.
			* Show probabilities.
	The widget may be requested to display value distributions only for instances of certain class (Group by). Show relative frequencies will scale the data by percentage of the dataset.		* Save image saves the graph to your computer in a .svg or .png format.
			* Produce a report.
	Show probabilities.
	Save image saves the graph to your computer in a .svg or .png format.
	Produce a report.

	For continuous attributes, the attribute values are displayed as a function graph. Class probabilities for continuous attributes are obtained with Gaussian kernel density estimation, while the appearance of the curve is set with the Precision bar (smooth or precise).		For continuous attributes, the attribute values are displayed as a function graph. Class probabilities for continuous attributes are obtained with Gaussian kernel density estimation, while the appearance of the curve is set with the Precision bar (smooth or precise).

Onnowpurbo at 03:32, 22 January 2020

2020-01-22T03:32:37Z

← Older revision		Revision as of 03:32, 22 January 2020
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	For discrete attributes, the graph displayed by the widget shows how many times (e.g., in how many instances) each attribute value appears in the data. If the data contains a class variable, class distributions for each of the attribute values will be displayed as well (like in the snapshot below). In order to create this graph, we used the Zoo dataset.		For discrete attributes, the graph displayed by the widget shows how many times (e.g., in how many instances) each attribute value appears in the data. If the data contains a class variable, class distributions for each of the attribute values will be displayed as well (like in the snapshot below). In order to create this graph, we used the Zoo dataset.

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

	A list of variables for distributions display		A list of variables for distributions display
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	Show probabilities.		Show probabilities.

	Save image saves the graph to your computer in a .svg or .png format.		Save image saves the graph to your computer in a .svg or .png format.

	Produce a report.		Produce a report.

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	For the purpose of this example, we used the Iris dataset.		For the purpose of this example, we used the Iris dataset.

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

	In class-less domains, the bars are displayed in gray. Here we set Bin continuous variables into 10 bins, which distributes variables into 10 intervals and displays averages of these intervals as histograms (see 2. above). We used the Housing dataset.		In class-less domains, the bars are displayed in gray. Here we set Bin continuous variables into 10 bins, which distributes variables into 10 intervals and displays averages of these intervals as histograms (see 2. above). We used the Housing dataset.

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

Onnowpurbo: Created page with "Sumber: https://docs.biolab.si//3/visual-programming/widgets/visualize/distributions.html Displays value distributions for a single attribute. Inputs Data: input datas..."

2020-01-12T06:46:58Z

Created page with "Sumber: https://docs.biolab.si//3/visual-programming/widgets/visualize/distributions.html Displays value distributions for a single attribute. Inputs Data: input datas..."

New page

Sumber: https://docs.biolab.si//3/visual-programming/widgets/visualize/distributions.html

Displays value distributions for a single attribute.

Inputs

Data: input dataset

The Distributions widget displays the value distribution of discrete or continuous attributes. If the data contains a class variable, distributions may be conditioned on the class.

For discrete attributes, the graph displayed by the widget shows how many times (e.g., in how many instances) each attribute value appears in the data. If the data contains a class variable, class distributions for each of the attribute values will be displayed as well (like in the snapshot below). In order to create this graph, we used the Zoo dataset.

../../_images/Distributions-Disc-stamped.png

A list of variables for distributions display

If Bin continuous variables is ticked, the widget will discretize continuous variables by assigning them to intervals. The number of intervals is set by precision scale. Alternatively, you can set smoothness for the distribution curves of continuous variables.

The widget may be requested to display value distributions only for instances of certain class (Group by). Show relative frequencies will scale the data by percentage of the dataset.

Show probabilities.

Save image saves the graph to your computer in a .svg or .png format.

Produce a report.

For continuous attributes, the attribute values are displayed as a function graph. Class probabilities for continuous attributes are obtained with Gaussian kernel density estimation, while the appearance of the curve is set with the Precision bar (smooth or precise).

For the purpose of this example, we used the Iris dataset.

../../_images/Distributions-Cont.png

In class-less domains, the bars are displayed in gray. Here we set Bin continuous variables into 10 bins, which distributes variables into 10 intervals and displays averages of these intervals as histograms (see 2. above). We used the Housing dataset.

../../_images/Distributions-NoClass.png

==Referensi==

* https://docs.biolab.si//3/visual-programming/widgets/visualize/distributions.html

==Pranala Menarik==

* [[Orange]]