Orange: Heat Map - Revision history

Onnowpurbo: /* Output */

2020-03-04T04:42:06Z

Output

← Older revision		Revision as of 04:42, 4 March 2020
Line 12:		Line 12:
	Selected Data: instances selected from the plot		Selected Data: instances selected from the plot

	Heat map adalah metode grafis untuk memvisualisasikan nilai atribut berdasarkan class dalam two-way matrix. Heat map hanya dapat digunakan pada dataset yang berisi variabel kontinu. Nilai-nilai diwakili oleh warna: semakin tinggi nilai tertentu, semakin gelap warna yang diwakili. Dengan menggabungkan class dan atribut pada sumbu x dan y, kita melihat di mana nilai atribut ~~mana~~ yang terkuat dan terlemah, sehingga memungkinkan kita untuk menemukan feature typical (diskrit) atau rentang nilai (kontinu) untuk setiap class.		Heat map adalah metode grafis untuk memvisualisasikan nilai atribut berdasarkan class dalam two-way matrix. Heat map hanya dapat digunakan pada dataset yang berisi variabel kontinu. Nilai-nilai diwakili oleh warna: semakin tinggi nilai tertentu, semakin gelap warna yang diwakili. Dengan menggabungkan class dan atribut pada sumbu x dan y, kita melihat di mana nilai atribut yang terkuat dan terlemah, sehingga memungkinkan kita untuk menemukan feature typical (diskrit) atau rentang nilai (kontinu) untuk setiap class.

	[[File:HeatMap-stamped.png\|center\|200px\|thumb]]		[[File:HeatMap-stamped.png\|center\|200px\|thumb]]

Onnowpurbo: /* Output */

2020-03-04T04:28:13Z

Output

← Older revision		Revision as of 04:28, 4 March 2020
Line 12:		Line 12:
	Selected Data: instances selected from the plot		Selected Data: instances selected from the plot

	Heat map adalah metode grafis untuk memvisualisasikan nilai atribut berdasarkan class dalam two-way matrix. Heat map hanya dapat digunakan pada dataset yang berisi variabel kontinu. Nilai-nilai diwakili oleh warna: semakin tinggi nilai tertentu, semakin gelap warna yang diwakili. Dengan menggabungkan ~~kelas~~ dan atribut pada sumbu x dan y, kita melihat di mana nilai atribut ~~adalah~~ yang terkuat dan ~~di mana~~ terlemah, sehingga memungkinkan kita untuk menemukan feature typical (diskrit) atau rentang nilai (kontinu) untuk setiap class.		Heat map adalah metode grafis untuk memvisualisasikan nilai atribut berdasarkan class dalam two-way matrix. Heat map hanya dapat digunakan pada dataset yang berisi variabel kontinu. Nilai-nilai diwakili oleh warna: semakin tinggi nilai tertentu, semakin gelap warna yang diwakili. Dengan menggabungkan class dan atribut pada sumbu x dan y, kita melihat di mana nilai atribut mana yang terkuat dan terlemah, sehingga memungkinkan kita untuk menemukan feature typical (diskrit) atau rentang nilai (kontinu) untuk setiap class.

	[[File:HeatMap-stamped.png\|center\|200px\|thumb]]		[[File:HeatMap-stamped.png\|center\|200px\|thumb]]

Onnowpurbo: /* Output */

2020-03-04T03:29:19Z

Output

← Older revision		Revision as of 03:29, 4 March 2020
Line 12:		Line 12:
	Selected Data: instances selected from the plot		Selected Data: instances selected from the plot

	Heat map adalah metode grafis untuk memvisualisasikan nilai atribut berdasarkan class dalam two-way matrix. ~~Ini~~ hanya ~~bekerja~~ pada dataset yang berisi variabel kontinu. Nilai-nilai diwakili oleh warna: semakin tinggi nilai tertentu, semakin gelap warna yang diwakili. Dengan menggabungkan kelas dan atribut pada sumbu x dan y, kita melihat di mana nilai atribut adalah yang terkuat dan di mana terlemah, sehingga memungkinkan kita untuk menemukan feature typical (diskrit) atau rentang nilai (kontinu) untuk setiap class.		Heat map adalah metode grafis untuk memvisualisasikan nilai atribut berdasarkan class dalam two-way matrix. Heat map hanya dapat digunakan pada dataset yang berisi variabel kontinu. Nilai-nilai diwakili oleh warna: semakin tinggi nilai tertentu, semakin gelap warna yang diwakili. Dengan menggabungkan kelas dan atribut pada sumbu x dan y, kita melihat di mana nilai atribut adalah yang terkuat dan di mana terlemah, sehingga memungkinkan kita untuk menemukan feature typical (diskrit) atau rentang nilai (kontinu) untuk setiap class.

	[[File:HeatMap-stamped.png\|center\|200px\|thumb]]		[[File:HeatMap-stamped.png\|center\|200px\|thumb]]

Onnowpurbo at 01:21, 3 February 2020

2020-02-03T01:21:24Z

← Older revision		Revision as of 01:21, 3 February 2020
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	~~Plots a~~ heat map ~~for a pair of attributes~~.		Plot heat map untuk sepasang atribut.

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

	Heat map ~~is a graphical method for visualizing attribute values by~~ class ~~in a~~ two-way matrix. ~~It only works on datasets containing continuous variables~~. ~~The values are represented by color~~: ~~the higher a certain value is~~, ~~the darker the represented color~~. ~~By combining class and attributes on~~ x ~~and~~ y ~~axes~~, ~~we see where the attribute values are the strongest and where the weakest~~, ~~thus enabling us to find~~ typical ~~features~~ (~~discrete~~) ~~or value range~~ (~~continuous~~) ~~for each~~ class.		Heat map adalah metode grafis untuk memvisualisasikan nilai atribut berdasarkan class dalam two-way matrix. Ini hanya bekerja pada dataset yang berisi variabel kontinu. Nilai-nilai diwakili oleh warna: semakin tinggi nilai tertentu, semakin gelap warna yang diwakili. Dengan menggabungkan kelas dan atribut pada sumbu x dan y, kita melihat di mana nilai atribut adalah yang terkuat dan di mana terlemah, sehingga memungkinkan kita untuk menemukan feature typical (diskrit) atau rentang nilai (kontinu) untuk setiap class.

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

	~~The~~ Heat Map ~~below displays attribute values for the~~ Housing ~~dataset~~. ~~The aforementioned dataset concerns the housing values in the suburbs of~~ Boston.		Heat Map di bawah ini menampilkan nilai atribut untuk dataset Housing. Dataset yang disebutkan di atas menyangkut nilai rumah di pinggiran kota Boston.

	~~The first thing we see in the map are the~~ ‘B’ ~~and ‘Tax’ attributes~~, ~~which are the only two colored in dark orange~~. ~~The~~ ‘B’ ~~attribute provides information on the proportion of blacks by town and the ‘Tax’ attribute informs us about the~~ full-value property-tax rate per $10,000. ~~In order to get a clearer~~ heat map, ~~we then use the~~ Select Columns widget ~~and remove the two attributes from the~~ dataset. ~~Then we again feed the~~ data ~~to the Heat~~ map. ~~The new projection offers additional information~~.		Hal pertama yang kita lihat di peta adalah atribut ‘B’ dan ‘Pajak’, yang merupakan dua warna oranye gelap. Atribut ‘B’ memberikan informasi tentang proporsi orang kulit hitam menurut kota dan atribut ‘Pajak’ memberi tahu kita tentang full-value property-tax rate per $10.000. Untuk mendapatkan heat map yang lebih jelas, kami kemudian menggunakan Select Columns widget dan menghapus dua atribut dari dataset. Kemudian kita kembali mengumpankan data ke heat map. Proyeksi baru ini menawarkan informasi tambahan.

	~~By removing~~ ‘B’ ~~and ‘Tax’~~, ~~we can see other deciding factors~~, ~~namely ‘Age’ and ‘ZN’~~. ~~The~~ ‘Age’ ~~attribute provides information on the proportion of owner-occupied units built prior to~~ 1940 ~~and the~~ ‘ZN’ ~~attribute informs us about the proportion of~~ non-retail business acres per town.		Dengan menghapus ‘B’ dan ‘Pajak’, kita dapat melihat faktor penentu lainnya, yaitu 'Age’ dan ‘ZN ’. Atribut ‘Age’ memberikan informasi tentang proporsi unit yang ditempati pemilik yang dibangun sebelum 1940 dan atribut ‘ZN’ memberi tahu kita tentang proporsi non-retail business acres per town.

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


	~~The~~ Heat Map widget ~~is a nice tool for discovering relevant features in the~~ data. ~~By removing some of the more pronounced features~~, ~~we came across new information~~, ~~which was hiding in the background~~.		Heat Map widget adalah alat yang bagus untuk menemukan feature yang relevan dalam data. Dengan menghapus beberapa feature yang lebih menonjol, kita menemukan informasi baru, yang bersembunyi di belakang.

	==Referensi==		==Referensi==

Onnowpurbo at 01:31, 28 January 2020

2020-01-28T01:31:01Z

← Older revision		Revision as of 01:31, 28 January 2020
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	Plots a heat map for a pair of attributes.		Plots a heat map for a pair of attributes.

	~~Inputs~~		==Input==

	Data: input dataset		Data: input dataset

	~~Outputs~~		==Output==

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

	Heat map is a graphical method for visualizing attribute values by class in a two-way matrix. It only works on datasets containing continuous variables. The values are represented by color: the higher a certain value is, the darker the represented color. By combining class and attributes on x and y axes, we see where the attribute values are the strongest and where the weakest, thus enabling us to find typical features (discrete) or value range (continuous) for each class.		Heat map is a graphical method for visualizing attribute values by class in a two-way matrix. It only works on datasets containing continuous variables. The values are represented by color: the higher a certain value is, the darker the represented color. By combining class and attributes on x and y axes, we see where the attribute values are the strongest and where the weakest, thus enabling us to find typical features (discrete) or value range (continuous) for each class.
Line 16:		Line 16:
	[[File:HeatMap-stamped.png\|center\|200px\|thumb]]		[[File:HeatMap-stamped.png\|center\|200px\|thumb]]

	The color scheme legend. Low and High are thresholds for the color palette (low for attributes with low values and high for attributes with high values).		* The color scheme legend. Low and High are thresholds for the color palette (low for attributes with low values and high for attributes with high values).
			* Merge data.
	Merge data.		* Sort columns and rows:
			** No Sorting (lists attributes as found in the dataset)
	Sort columns and rows:		** Clustering (clusters data by similarity)
			** Clustering with ordered leaves (maximizes the sum of similarities of adjacent elements)
	No Sorting (lists attributes as found in the dataset)		* Set what is displayed in the plot in Annotation & Legend.
	Clustering (clusters data by similarity)		** If Show legend is ticked, a color chart will be displayed above the map.
	Clustering with ordered leaves (maximizes the sum of similarities of adjacent elements)		** If Stripes with averages is ticked, a new line with attribute averages will be displayed on the left.
			** Row Annotations adds annotations to each instance on the right.
	Set what is displayed in the plot in Annotation & Legend.		** Column Label Positions places column labels in a selected place (None, Top, Bottom, Top and Bottom).
			* If Keep aspect ratio is ticked, each value will be displayed with a square (proportionate to the map).
	If Show legend is ticked, a color chart will be displayed above the map.		* If Send Automatically is ticked, changes are communicated automatically. Alternatively, click Send.
	If Stripes with averages is ticked, a new line with attribute averages will be displayed on the left.		* Save image saves the image to your computer in a .svg or .png format.
	Row Annotations adds annotations to each instance on the right.		* Produce a report.
	Column Label Positions places column labels in a selected place (None, Top, Bottom, Top and Bottom).

	If Keep aspect ratio is ticked, each value will be displayed with a square (proportionate to the map).

	If Send Automatically is ticked, changes are communicated automatically. Alternatively, click Send.

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

	Produce a report.

	==Contoh==		==Contoh==

Onnowpurbo at 03:59, 22 January 2020

2020-01-22T03:59:14Z

← Older revision		Revision as of 03:59, 22 January 2020
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	Heat map is a graphical method for visualizing attribute values by class in a two-way matrix. It only works on datasets containing continuous variables. The values are represented by color: the higher a certain value is, the darker the represented color. By combining class and attributes on x and y axes, we see where the attribute values are the strongest and where the weakest, thus enabling us to find typical features (discrete) or value range (continuous) for each class.		Heat map is a graphical method for visualizing attribute values by class in a two-way matrix. It only works on datasets containing continuous variables. The values are represented by color: the higher a certain value is, the darker the represented color. By combining class and attributes on x and y axes, we see where the attribute values are the strongest and where the weakest, thus enabling us to find typical features (discrete) or value range (continuous) for each class.

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

	The color scheme legend. Low and High are thresholds for the color palette (low for attributes with low values and high for attributes with high values).		The color scheme legend. Low and High are thresholds for the color palette (low for attributes with low values and high for attributes with high values).
Line 23:		Line 23:

	No Sorting (lists attributes as found in the dataset)		No Sorting (lists attributes as found in the dataset)

	Clustering (clusters data by similarity)		Clustering (clusters data by similarity)

	Clustering with ordered leaves (maximizes the sum of similarities of adjacent elements)		Clustering with ordered leaves (maximizes the sum of similarities of adjacent elements)

Line 31:		Line 29:

	If Show legend is ticked, a color chart will be displayed above the map.		If Show legend is ticked, a color chart will be displayed above the map.

	If Stripes with averages is ticked, a new line with attribute averages will be displayed on the left.		If Stripes with averages is ticked, a new line with attribute averages will be displayed on the left.

	Row Annotations adds annotations to each instance on the right.		Row Annotations adds annotations to each instance on the right.

	Column Label Positions places column labels in a selected place (None, Top, Bottom, Top and Bottom).		Column Label Positions places column labels in a selected place (None, Top, Bottom, Top and Bottom).

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	Produce a report.		Produce a report.

	~~Example~~		==Contoh==

	The Heat Map below displays attribute values for the Housing dataset. The aforementioned dataset concerns the housing values in the suburbs of Boston.		The Heat Map below displays attribute values for the Housing dataset. The aforementioned dataset concerns the housing values in the suburbs of Boston.
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	By removing ‘B’ and ‘Tax’, we can see other deciding factors, namely ‘Age’ and ‘ZN’. The ‘Age’ attribute provides information on the proportion of owner-occupied units built prior to 1940 and the ‘ZN’ attribute informs us about the proportion of non-retail business acres per town.		By removing ‘B’ and ‘Tax’, we can see other deciding factors, namely ‘Age’ and ‘ZN’. The ‘Age’ attribute provides information on the proportion of owner-occupied units built prior to 1940 and the ‘ZN’ attribute informs us about the proportion of non-retail business acres per town.

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


	The Heat Map widget is a nice tool for discovering relevant features in the data. By removing some of the more pronounced features, we came across new information, which was hiding in the background.		The Heat Map widget is a nice tool for discovering relevant features in the data. By removing some of the more pronounced features, we came across new information, which was hiding in the background.
	~~References~~

	~~Housing~~ Dataset		==Referensi==

			* [https://archive.ics.uci.edu/ml/datasets.php Dataset]

Onnowpurbo: Created page with "Sumber: https://docs.biolab.si//3/visual-programming/widgets/visualize/heatmap.html Plots a heat map for a pair of attributes. Inputs Data: input dataset Outputs..."

2020-01-12T06:49:50Z

Created page with "Sumber: https://docs.biolab.si//3/visual-programming/widgets/visualize/heatmap.html Plots a heat map for a pair of attributes. Inputs Data: input dataset Outputs..."

New page

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

Plots a heat map for a pair of attributes.

Inputs

Data: input dataset

Outputs

Selected Data: instances selected from the plot

Heat map is a graphical method for visualizing attribute values by class in a two-way matrix. It only works on datasets containing continuous variables. The values are represented by color: the higher a certain value is, the darker the represented color. By combining class and attributes on x and y axes, we see where the attribute values are the strongest and where the weakest, thus enabling us to find typical features (discrete) or value range (continuous) for each class.

../../_images/HeatMap-stamped.png

The color scheme legend. Low and High are thresholds for the color palette (low for attributes with low values and high for attributes with high values).

Merge data.

Sort columns and rows:

No Sorting (lists attributes as found in the dataset)

Clustering (clusters data by similarity)

Clustering with ordered leaves (maximizes the sum of similarities of adjacent elements)

Set what is displayed in the plot in Annotation & Legend.

If Show legend is ticked, a color chart will be displayed above the map.

If Stripes with averages is ticked, a new line with attribute averages will be displayed on the left.

Row Annotations adds annotations to each instance on the right.

Column Label Positions places column labels in a selected place (None, Top, Bottom, Top and Bottom).

If Keep aspect ratio is ticked, each value will be displayed with a square (proportionate to the map).

If Send Automatically is ticked, changes are communicated automatically. Alternatively, click Send.

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

Produce a report.

Example

The Heat Map below displays attribute values for the Housing dataset. The aforementioned dataset concerns the housing values in the suburbs of Boston.

The first thing we see in the map are the ‘B’ and ‘Tax’ attributes, which are the only two colored in dark orange. The ‘B’ attribute provides information on the proportion of blacks by town and the ‘Tax’ attribute informs us about the full-value property-tax rate per $10,000. In order to get a clearer heat map, we then use the Select Columns widget and remove the two attributes from the dataset. Then we again feed the data to the Heat map. The new projection offers additional information.

By removing ‘B’ and ‘Tax’, we can see other deciding factors, namely ‘Age’ and ‘ZN’. The ‘Age’ attribute provides information on the proportion of owner-occupied units built prior to 1940 and the ‘ZN’ attribute informs us about the proportion of non-retail business acres per town.

../../_images/HeatMap-Example1.png

The Heat Map widget is a nice tool for discovering relevant features in the data. By removing some of the more pronounced features, we came across new information, which was hiding in the background.
References

Housing Dataset

==Referensi==

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

==Pranala Menarik==

* [[Orange]]