Orange: Box Plot - Revision history

Onnowpurbo: /* Contoh */

2020-04-08T21:18:23Z

Contoh

← Older revision		Revision as of 21:18, 8 April 2020
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	Contoh selanjutnya, kita menggunakan zoo data dan membuat workflow clustering dengan Distances and Hierarchical Clustering. Sekarang tentukan threshold untuk pemilihan cluster (klik pada ruler di atas). Hubungkan Box Plot ke Hierarchical Clustering, centang Order by relevance dan pilih Cluster as a subgroup. Ini akan mengurut atribut dengan seberapa baik mereka mendefinisikan subgroup yang dipilih, dalam kasus kita sebuah cluster. Sepertinya cluster kita memang sangat cocok dengan jenis hewan!		Contoh selanjutnya, kita menggunakan zoo data dan membuat workflow clustering dengan Distances and Hierarchical Clustering. Sekarang tentukan threshold untuk pemilihan cluster (klik pada ruler di atas). Hubungkan Box Plot ke Hierarchical Clustering, centang Order by relevance dan pilih Cluster as a subgroup. Ini akan mengurut atribut dengan seberapa baik mereka mendefinisikan subgroup yang dipilih, dalam kasus kita sebuah cluster. Sepertinya cluster kita memang sangat cocok dengan jenis hewan!

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

	==Referensi==		==Referensi==

Onnowpurbo: /* Contoh */

2020-04-08T21:18:09Z

Contoh

← Older revision		Revision as of 21:18, 8 April 2020
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	Widget Box Plot paling umum digunakan segera setelah File widget untuk mengamati properti statistik dari suatu dataset. Dalam contoh ini, kita telah menggunakan heart-disease data untuk memeriksa variabel kita.		Widget Box Plot paling umum digunakan segera setelah File widget untuk mengamati properti statistik dari suatu dataset. Dalam contoh ini, kita telah menggunakan heart-disease data untuk memeriksa variabel kita.

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

	Box Plot juga berguna untuk menemukan properti dari dataset tertentu, misalnya satu set instance yang ditentukan secara manual di widget lain (misalnya Scatter Plot, atau instance dari beberapa cluster, atau node klasifikasi tree).		Box Plot juga berguna untuk menemukan properti dari dataset tertentu, misalnya satu set instance yang ditentukan secara manual di widget lain (misalnya Scatter Plot, atau instance dari beberapa cluster, atau node klasifikasi tree).

Onnowpurbo: /* Contoh */

2020-03-04T01:56:34Z

Contoh

← Older revision		Revision as of 01:56, 4 March 2020
Line 40:		Line 40:
	Box Plot juga berguna untuk menemukan properti dari dataset tertentu, misalnya satu set instance yang ditentukan secara manual di widget lain (misalnya Scatter Plot, atau instance dari beberapa cluster, atau node klasifikasi tree).		Box Plot juga berguna untuk menemukan properti dari dataset tertentu, misalnya satu set instance yang ditentukan secara manual di widget lain (misalnya Scatter Plot, atau instance dari beberapa cluster, atau node klasifikasi tree).

	~~Congtoh~~ selanjutnya, kita menggunakan zoo data dan membuat workflow clustering dengan Distances and Hierarchical Clustering. Sekarang tentukan threshold untuk pemilihan cluster (klik pada ruler di atas). Hubungkan Box Plot ke Hierarchical Clustering, centang Order by relevance dan pilih Cluster as a subgroup. Ini akan mengurut atribut dengan seberapa baik mereka mendefinisikan subgroup yang dipilih, dalam kasus kita sebuah cluster. Sepertinya cluster kita memang sangat cocok dengan jenis hewan!		Contoh selanjutnya, kita menggunakan zoo data dan membuat workflow clustering dengan Distances and Hierarchical Clustering. Sekarang tentukan threshold untuk pemilihan cluster (klik pada ruler di atas). Hubungkan Box Plot ke Hierarchical Clustering, centang Order by relevance dan pilih Cluster as a subgroup. Ini akan mengurut atribut dengan seberapa baik mereka mendefinisikan subgroup yang dipilih, dalam kasus kita sebuah cluster. Sepertinya cluster kita memang sangat cocok dengan jenis hewan!

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

Onnowpurbo: /* Contoh */

2020-03-04T01:56:20Z

Contoh

← Older revision		Revision as of 01:56, 4 March 2020
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	==Contoh==		==Contoh==

	Widget Box Plot paling umum digunakan segera setelah File widget untuk mengamati properti statistik dari suatu dataset. Dalam contoh ~~pertama~~, kita telah menggunakan heart-disease data untuk memeriksa variabel kita.		Widget Box Plot paling umum digunakan segera setelah File widget untuk mengamati properti statistik dari suatu dataset. Dalam contoh ini, kita telah menggunakan heart-disease data untuk memeriksa variabel kita.

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

	Box Plot juga berguna untuk menemukan properti dari dataset tertentu, misalnya satu set instance yang ditentukan secara manual di widget lain (misalnya Scatter Plot atau instance dari beberapa cluster atau node tree ~~klasifikasi~~)~~. Mari sekarang kita menggunakan zoo data dan membuat workflow clustering dengan Distances and Hierarchical Clustering~~.		Box Plot juga berguna untuk menemukan properti dari dataset tertentu, misalnya satu set instance yang ditentukan secara manual di widget lain (misalnya Scatter Plot, atau instance dari beberapa cluster, atau node klasifikasi tree).

	Sekarang tentukan threshold untuk pemilihan cluster (klik pada ruler di atas). Hubungkan Box Plot ke Hierarchical Clustering, centang Order by relevance dan pilih Cluster as a subgroup. Ini akan mengurut atribut dengan seberapa baik mereka mendefinisikan subgroup yang dipilih, dalam kasus kita sebuah cluster. Sepertinya cluster kita memang sangat cocok dengan jenis hewan!		Congtoh selanjutnya, kita menggunakan zoo data dan membuat workflow clustering dengan Distances and Hierarchical Clustering. Sekarang tentukan threshold untuk pemilihan cluster (klik pada ruler di atas). Hubungkan Box Plot ke Hierarchical Clustering, centang Order by relevance dan pilih Cluster as a subgroup. Ini akan mengurut atribut dengan seberapa baik mereka mendefinisikan subgroup yang dipilih, dalam kasus kita sebuah cluster. Sepertinya cluster kita memang sangat cocok dengan jenis hewan!

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



	==Referensi==		==Referensi==

Onnowpurbo at 00:48, 3 February 2020

2020-02-03T00:48:04Z

← Older revision		Revision as of 00:48, 3 February 2020
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	[[File:BoxPlot-Example1.png\|center\|200px\|thumb]]		[[File:BoxPlot-Example1.png\|center\|200px\|thumb]]

	Box Plot ~~is also useful for finding the properties of a specific~~ dataset, ~~for~~ instance ~~a set of instances manually defined in another~~ widget (~~e.g.~~ Scatter Plot ~~or instances belonging to some~~ cluster ~~or a classification~~ tree ~~node~~. ~~Let us now use~~ zoo data ~~and create a typical~~ clustering ~~workflow with~~ Distances and Hierarchical Clustering.		Box Plot juga berguna untuk menemukan properti dari dataset tertentu, misalnya satu set instance yang ditentukan secara manual di widget lain (misalnya Scatter Plot atau instance dari beberapa cluster atau node tree klasifikasi). Mari sekarang kita menggunakan zoo data dan membuat workflow clustering dengan Distances and Hierarchical Clustering.

	~~Now define the~~ threshold ~~for~~ cluster ~~selection~~ (~~click on the~~ ruler ~~at the top~~). ~~Connect~~ Box Plot to Hierarchical Clustering, ~~tick~~ Order by relevance ~~and select~~ Cluster as a subgroup. ~~This will order attributes by how well they define the selected~~ subgroup, ~~in our case a~~ cluster. ~~Seems like our clusters indeed correspond very well with the animal type~~!		Sekarang tentukan threshold untuk pemilihan cluster (klik pada ruler di atas). Hubungkan Box Plot ke Hierarchical Clustering, centang Order by relevance dan pilih Cluster as a subgroup. Ini akan mengurut atribut dengan seberapa baik mereka mendefinisikan subgroup yang dipilih, dalam kasus kita sebuah cluster. Sepertinya cluster kita memang sangat cocok dengan jenis hewan!

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

Onnowpurbo at 00:43, 3 February 2020

2020-02-03T00:43:49Z

← Older revision		Revision as of 00:43, 3 February 2020
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	Sumber: https://docs.biolab.si//3/visual-programming/widgets/visualize/boxplot.html		Sumber: https://docs.biolab.si//3/visual-programming/widgets/visualize/boxplot.html

	~~Shows distribution of attribute values~~.		Menunjukkan distribusi nilai atribut.

	==Input==		==Input==
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	Data: data with an additional column showing whether a point is selected		Data: data with an additional column showing whether a point is selected

	~~The~~ Box Plot ~~widget shows the distributions of attribute values~~. ~~It is a good practice to check any new~~ data ~~with this~~ widget ~~to quickly discover any anomalies~~, ~~such as duplicated values~~ (~~e.g~~. gray ~~and~~ grey), outliers, ~~and alike~~.		Widget Box Plot menunjukkan distribusi nilai atribut. Merupakan praktik yang baik untuk memeriksa data baru apa pun dengan widget ini untuk menemukan anomali dengan cepat, seperti nilai duplikat (mis. gray atau grey), outliers, dan sejenisnya.

	[[File:BoxPlot-Continuous-stamped.png\|center\|200px\|thumb]]		[[File:BoxPlot-Continuous-stamped.png\|center\|200px\|thumb]]
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	* Access help, save image or produce a report.		* Access help, save image or produce a report.

	~~For discrete attributes~~, ~~the bars represent the number of instances with each particular attribute value~~. ~~The plot shows the number of different animal types in the~~ Zoo ~~dataset~~: ~~there are~~ 41 ~~mammals~~, 13 ~~fish~~, 20 ~~birds and so on~~.		Untuk atribut diskrit, bar akan mewakili jumlah instance dengan setiap nilai atribut tertentu. Plot menunjukkan jumlah jenis hewan yang berbeda dalam dataset Zoo: ada 41 mamalia, 13 ikan, 20 burung dan sebagainya.

	[[File:BoxPlot-Discrete.png\|center\|200px\|thumb]]		[[File:BoxPlot-Discrete.png\|center\|200px\|thumb]]
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	==Contoh==		==Contoh==

	~~The~~ Box Plot ~~widget is most commonly used immediately after the~~ File widget ~~to observe the statistical properties of a~~ dataset. ~~In the first example~~, ~~we have used~~ heart-disease data ~~to inspect our variables~~.		Widget Box Plot paling umum digunakan segera setelah File widget untuk mengamati properti statistik dari suatu dataset. Dalam contoh pertama, kita telah menggunakan heart-disease data untuk memeriksa variabel kita.

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

Onnowpurbo at 01:27, 28 January 2020

2020-01-28T01:27:11Z

← Older revision		Revision as of 01:27, 28 January 2020
Line 3:		Line 3:
	Shows distribution of attribute values.		Shows distribution of attribute values.

	~~Inputs~~		==Input==

	Data: input dataset		Data: input dataset

	~~Outputs~~		==Output==

	Selected Data: instances selected from the plot		Selected Data: instances selected from the plot
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	[[File:BoxPlot-Continuous-stamped.png\|center\|200px\|thumb]]		[[File:BoxPlot-Continuous-stamped.png\|center\|200px\|thumb]]

	Select the variable you want to plot. Tick Order by relevance to order variables by Chi2 or ANOVA over the selected subgroup.		* Select the variable you want to plot. Tick Order by relevance to order variables by Chi2 or ANOVA over the selected subgroup.
			* Choose Subgroups to see box plots displayed by a discrete subgroup.
	Choose Subgroups to see box plots displayed by a discrete subgroup.		* When instances are grouped by a subgroup, you can change the display mode. Annotated boxes will display the end values, the mean and the median, while compare medians and compare means will, naturally, compare the selected value between subgroups. continuous

	When instances are grouped by a subgroup, you can change the display mode. Annotated boxes will display the end values, the mean and the median, while compare medians and compare means will, naturally, compare the selected value between subgroups. continuous

	[[File:BoxPlot-Continuous-small.png\|center\|200px\|thumb]]		[[File:BoxPlot-Continuous-small.png\|center\|200px\|thumb]]

	The mean (the dark blue vertical line). The thin blue line represents the standard deviation.		* The mean (the dark blue vertical line). The thin blue line represents the standard deviation.
			* Values of the first (25%) and the third (75%) quantile. The blue highlighted area represents the values between the first and the third quartile.
	Values of the first (25%) and the third (75%) quantile. The blue highlighted area represents the values between the first and the third quartile.		* The median (yellow vertical line).
			* If Send automatically is ticked, changes are communicated automatically. Alternatively, press Send.
	The median (yellow vertical line).		* Access help, save image or produce a report.
	If Send automatically is ticked, changes are communicated automatically. Alternatively, press Send.
	Access help, save image or produce a report.

	For discrete attributes, the bars represent the number of instances with each particular attribute value. The plot shows the number of different animal types in the Zoo dataset: there are 41 mammals, 13 fish, 20 birds and so on.		For discrete attributes, the bars represent the number of instances with each particular attribute value. The plot shows the number of different animal types in the Zoo dataset: there are 41 mammals, 13 fish, 20 birds and so on.

Onnowpurbo at 03:25, 22 January 2020

2020-01-22T03:25:09Z

← Older revision		Revision as of 03:25, 22 January 2020
Line 10:		Line 10:

	Selected Data: instances selected from the plot		Selected Data: instances selected from the plot

	Data: data with an additional column showing whether a point is selected		Data: data with an additional column showing whether a point is selected

	The Box Plot widget shows the distributions of attribute values. It is a good practice to check any new data with this widget to quickly discover any anomalies, such as duplicated values (e.g. gray and grey), outliers, and alike.		The Box Plot widget shows the distributions of attribute values. It is a good practice to check any new data with this widget to quickly discover any anomalies, such as duplicated values (e.g. gray and grey), outliers, and alike.

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

	Select the variable you want to plot. Tick Order by relevance to order variables by Chi2 or ANOVA over the selected subgroup.		Select the variable you want to plot. Tick Order by relevance to order variables by Chi2 or ANOVA over the selected subgroup.
Line 22:		Line 21:

	When instances are grouped by a subgroup, you can change the display mode. Annotated boxes will display the end values, the mean and the median, while compare medians and compare means will, naturally, compare the selected value between subgroups. continuous		When instances are grouped by a subgroup, you can change the display mode. Annotated boxes will display the end values, the mean and the median, while compare medians and compare means will, naturally, compare the selected value between subgroups. continuous

			[[File:BoxPlot-Continuous-small.png\|center\|200px\|thumb]]

	The mean (the dark blue vertical line). The thin blue line represents the standard deviation.		The mean (the dark blue vertical line). The thin blue line represents the standard deviation.
Line 28:		Line 29:

	The median (yellow vertical line).		The median (yellow vertical line).

	If Send automatically is ticked, changes are communicated automatically. Alternatively, press Send.		If Send automatically is ticked, changes are communicated automatically. Alternatively, press Send.

	Access help, save image or produce a report.		Access help, save image or produce a report.

	For discrete attributes, the bars represent the number of instances with each particular attribute value. The plot shows the number of different animal types in the Zoo dataset: there are 41 mammals, 13 fish, 20 birds and so on.		For discrete attributes, the bars represent the number of instances with each particular attribute value. The plot shows the number of different animal types in the Zoo dataset: there are 41 mammals, 13 fish, 20 birds and so on.

	~~../../_images/~~BoxPlot-Discrete.png		[[File:BoxPlot-Discrete.png\|center\|200px\|thumb]]
	~~Example~~
			==Contoh==

	The Box Plot widget is most commonly used immediately after the File widget to observe the statistical properties of a dataset. In the first example, we have used heart-disease data to inspect our variables.		The Box Plot widget is most commonly used immediately after the File widget to observe the statistical properties of a dataset. In the first example, we have used heart-disease data to inspect our variables.

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

	Box Plot is also useful for finding the properties of a specific dataset, for instance a set of instances manually defined in another widget (e.g. Scatter Plot or instances belonging to some cluster or a classification tree node. Let us now use zoo data and create a typical clustering workflow with Distances and Hierarchical Clustering.		Box Plot is also useful for finding the properties of a specific dataset, for instance a set of instances manually defined in another widget (e.g. Scatter Plot or instances belonging to some cluster or a classification tree node. Let us now use zoo data and create a typical clustering workflow with Distances and Hierarchical Clustering.
Line 46:		Line 46:
	Now define the threshold for cluster selection (click on the ruler at the top). Connect Box Plot to Hierarchical Clustering, tick Order by relevance and select Cluster as a subgroup. This will order attributes by how well they define the selected subgroup, in our case a cluster. Seems like our clusters indeed correspond very well with the animal type!		Now define the threshold for cluster selection (click on the ruler at the top). Connect Box Plot to Hierarchical Clustering, tick Order by relevance and select Cluster as a subgroup. This will order attributes by how well they define the selected subgroup, in our case a cluster. Seems like our clusters indeed correspond very well with the animal type!

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

Onnowpurbo: Created page with "Sumber: https://docs.biolab.si//3/visual-programming/widgets/visualize/boxplot.html Shows distribution of attribute values. Inputs Data: input dataset Outputs Sel..."

2020-01-12T06:42:54Z

Created page with "Sumber: https://docs.biolab.si//3/visual-programming/widgets/visualize/boxplot.html Shows distribution of attribute values. Inputs Data: input dataset Outputs Sel..."

New page

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

Shows distribution of attribute values.

Inputs

Data: input dataset

Outputs

Selected Data: instances selected from the plot

Data: data with an additional column showing whether a point is selected

The Box Plot widget shows the distributions of attribute values. It is a good practice to check any new data with this widget to quickly discover any anomalies, such as duplicated values (e.g. gray and grey), outliers, and alike.

../../_images/BoxPlot-Continuous-stamped.png

Select the variable you want to plot. Tick Order by relevance to order variables by Chi2 or ANOVA over the selected subgroup.

Choose Subgroups to see box plots displayed by a discrete subgroup.

When instances are grouped by a subgroup, you can change the display mode. Annotated boxes will display the end values, the mean and the median, while compare medians and compare means will, naturally, compare the selected value between subgroups. continuous

The mean (the dark blue vertical line). The thin blue line represents the standard deviation.

Values of the first (25%) and the third (75%) quantile. The blue highlighted area represents the values between the first and the third quartile.

The median (yellow vertical line).

If Send automatically is ticked, changes are communicated automatically. Alternatively, press Send.

Access help, save image or produce a report.

For discrete attributes, the bars represent the number of instances with each particular attribute value. The plot shows the number of different animal types in the Zoo dataset: there are 41 mammals, 13 fish, 20 birds and so on.

../../_images/BoxPlot-Discrete.png
Example

The Box Plot widget is most commonly used immediately after the File widget to observe the statistical properties of a dataset. In the first example, we have used heart-disease data to inspect our variables.

../../_images/BoxPlot-Example1.png

Box Plot is also useful for finding the properties of a specific dataset, for instance a set of instances manually defined in another widget (e.g. Scatter Plot or instances belonging to some cluster or a classification tree node. Let us now use zoo data and create a typical clustering workflow with Distances and Hierarchical Clustering.

Now define the threshold for cluster selection (click on the ruler at the top). Connect Box Plot to Hierarchical Clustering, tick Order by relevance and select Cluster as a subgroup. This will order attributes by how well they define the selected subgroup, in our case a cluster. Seems like our clusters indeed correspond very well with the animal type!

../../_images/BoxPlot-Example2.png

==Referensi==

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

==Pranala Menarik==

* [[Orange]]