Orange: Paint Data - Revision history

Onnowpurbo at 02:17, 16 April 2020

2020-04-16T02:17:10Z

← Older revision		Revision as of 02:17, 16 April 2020
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	~~Paints~~ data pada bidang 2D. ~~Anda~~ dapat menempatkan titik data individual atau menggunakan brush untuk paint dataset yang besar.		Widget Paint Data dapat melakukan paint data pada bidang 2D. Kita dapat menempatkan titik data individual atau menggunakan brush untuk paint dataset yang besar.

	==Output==		==Output==
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	Data: dataset as painted in the plot		Data: dataset as painted in the plot

	Widget mendukung pembuatan dataset baru dengan menempatkan titik data secara visual pada bidang dua dimensi. Point data dapat ditempatkan di bidang secara individual (Put) atau dalam jumlah yang lebih besar dengan brushing (Brush). Point data dapat menjadi milik class jika data tersebut dimaksudkan untuk digunakan dalam supervised learning.		Widget Paint Data mendukung pembuatan dataset baru dengan menempatkan titik data secara visual pada bidang dua dimensi. Point data dapat ditempatkan di bidang secara individual (Put) atau dalam jumlah yang lebih besar dengan brushing (Brush). Point data dapat menjadi milik class jika data tersebut dimaksudkan untuk digunakan dalam supervised learning.

	[[File:PaintData-stamped.png\|center\|~~200px~~\|thumb]]		[[File:PaintData-stamped.png\|center\|600px\|thumb]]

	* Name the axes and select a class to paint data instances. You can add or remove classes. Use only one class to create classless, unsupervised datasets.		* Name the axes and select a class to paint data instances. You can add or remove classes. Use only one class to create classless, unsupervised datasets.
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	==Contoh==		==Contoh==

	Dalam contoh di bawah ini, ~~kami telah~~ paint dataset dengan 4 class. Dataset seperti ~~itu bagus~~ untuk mendemonstrasikan metoda k-means and hierarchical clustering. Dalam screenshot, kita melihat bahwa k-Means, secara keseluruhan, mengenali cluster lebih baik daripada Hierarchical Clustering. Ini mengembalikan peringkat skor, di mana skor terbaik (yang dengan nilai tertinggi) berarti jumlah cluster yang paling mungkin. Namun, ~~pengelompokan hierarki~~ tidak mengelompokkan ~~kelas~~ yang tepat bersama. ~~Ini~~ adalah tool yang baik untuk belajar dan mengeksplorasi konsep statistik.		Dalam contoh di bawah ini, widget Paint Data digunakan untuk paint dataset dengan 4 class. Dataset seperti ini baik untuk mendemonstrasikan metoda widget k-means and widget hierarchical clustering. Dalam screenshot, kita melihat bahwa widget k-Means, secara keseluruhan, mengenali cluster lebih baik daripada widget Hierarchical Clustering. Ini mengembalikan peringkat skor, di mana skor terbaik (yang dengan nilai tertinggi) berarti jumlah cluster yang paling mungkin. Namun, widget Hierarchical Clustering tidak mengelompokkan class yang tepat bersama. Widget Paint Data adalah tool yang baik untuk belajar dan mengeksplorasi konsep statistik.

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

	==Referensi==		==Referensi==

Onnowpurbo: /* Contoh */

2020-04-04T06:37:31Z

Contoh

← Older revision		Revision as of 06:37, 4 April 2020
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	==Contoh==		==Contoh==

	Dalam contoh di bawah ini, kami telah paint dataset dengan 4 class. Dataset seperti itu bagus untuk mendemonstrasikan metoda k-means and hierarchical clustering. Dalam screenshot, kita melihat bahwa k-Means, secara keseluruhan, mengenali cluster lebih baik daripada Hierarchical Clustering. Ini mengembalikan peringkat skor, di mana skor terbaik (yang dengan nilai tertinggi) berarti jumlah cluster yang paling mungkin. Namun, pengelompokan hierarki tidak mengelompokkan kelas yang tepat bersama. Ini adalah ~~alat~~ yang baik untuk belajar dan mengeksplorasi konsep statistik.		Dalam contoh di bawah ini, kami telah paint dataset dengan 4 class. Dataset seperti itu bagus untuk mendemonstrasikan metoda k-means and hierarchical clustering. Dalam screenshot, kita melihat bahwa k-Means, secara keseluruhan, mengenali cluster lebih baik daripada Hierarchical Clustering. Ini mengembalikan peringkat skor, di mana skor terbaik (yang dengan nilai tertinggi) berarti jumlah cluster yang paling mungkin. Namun, pengelompokan hierarki tidak mengelompokkan kelas yang tepat bersama. Ini adalah tool yang baik untuk belajar dan mengeksplorasi konsep statistik.

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





	==Referensi==		==Referensi==

Onnowpurbo at 23:57, 29 January 2020

2020-01-29T23:57:57Z

← Older revision		Revision as of 23:57, 29 January 2020
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	Paints data ~~on a~~ 2D ~~plane~~. ~~You can place~~ individual ~~data points or use a~~ brush to paint ~~larger datasets~~.		Paints data pada bidang 2D. Anda dapat menempatkan titik data individual atau menggunakan brush untuk paint dataset yang besar.

	==Output==		==Output==

	Data: dataset as painted in the plot		Data: dataset as painted in the plot

	~~The widget supports the creation of a new~~ dataset ~~by visually placing~~ data ~~points on a two-dimension plane~~. ~~Data points can be placed on the plane individually~~ (Put) ~~or in a larger number by~~ brushing (Brush). ~~Data points can belong to classes if the~~ data ~~is intended to be used in~~ supervised learning.		Widget mendukung pembuatan dataset baru dengan menempatkan titik data secara visual pada bidang dua dimensi. Point data dapat ditempatkan di bidang secara individual (Put) atau dalam jumlah yang lebih besar dengan brushing (Brush). Point data dapat menjadi milik class jika data tersebut dimaksudkan untuk digunakan dalam supervised learning.

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

	~~In the example below~~, ~~we have painted a~~ dataset ~~with~~ 4 ~~classes~~. ~~Such dataset is great for demonstrating~~ k-means and hierarchical clustering ~~methods~~. ~~In the~~ screenshot, ~~we see that~~ k-Means, ~~overall~~, ~~recognizes clusters better than~~ Hierarchical Clustering. ~~It returns a score rank~~, ~~where the best score~~ (~~the one with the highest value~~) ~~means the most likely number of clusters~~. ~~Hierarchical clustering~~, ~~however, doesn’t group the right classes together~~. ~~This is a great tool for learning and exploring statistical concepts~~.		Dalam contoh di bawah ini, kami telah paint dataset dengan 4 class. Dataset seperti itu bagus untuk mendemonstrasikan metoda k-means and hierarchical clustering. Dalam screenshot, kita melihat bahwa k-Means, secara keseluruhan, mengenali cluster lebih baik daripada Hierarchical Clustering. Ini mengembalikan peringkat skor, di mana skor terbaik (yang dengan nilai tertinggi) berarti jumlah cluster yang paling mungkin. Namun, pengelompokan hierarki tidak mengelompokkan kelas yang tepat bersama. Ini adalah alat yang baik untuk belajar dan mengeksplorasi konsep statistik.

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

Onnowpurbo at 05:17, 27 January 2020

2020-01-27T05:17:08Z

← Older revision		Revision as of 05:17, 27 January 2020
Line 4:		Line 4:
	Paints data on a 2D plane. You can place individual data points or use a brush to paint larger datasets.		Paints data on a 2D plane. You can place individual data points or use a brush to paint larger datasets.

	~~Outputs~~		==Output==

	Data: dataset as painted in the plot		Data: dataset as painted in the plot
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	[[File:PaintData-stamped.png\|center\|200px\|thumb]]		[[File:PaintData-stamped.png\|center\|200px\|thumb]]

	Name the axes and select a class to paint data instances. You can add or remove classes. Use only one class to create classless, unsupervised datasets.		* Name the axes and select a class to paint data instances. You can add or remove classes. Use only one class to create classless, unsupervised datasets.
			* Drawing tools. Paint data points with Brush (multiple data instances) or Put (individual data instance). Select data points with Select and remove them with the Delete/Backspace key. Reposition data points with Jitter (spread) and Magnet (focus). Use Zoom and scroll to zoom in or out. Below, set the radius and intensity for Brush, Put, Jitter and Magnet tools.
			* Reset to Input Data.
			* Save Image saves the image to your computer in a .svg or .png format.
			* Produce a report.
			* Tick the box on the left to automatically commit changes to other widgets. Alternatively, press Send to apply them.

	Drawing tools. Paint data points with Brush (multiple data instances) or Put (individual data instance). Select data points with Select and remove them with the Delete/Backspace key. Reposition data points with Jitter (spread) and Magnet (focus). Use Zoom and scroll to zoom in or out. Below, set the radius and intensity for Brush, Put, Jitter and Magnet tools.		==Contoh==

	~~Reset to Input Data.~~

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

	~~Produce a report.~~

	~~Tick the box on the left to automatically commit changes to other widgets. Alternatively, press Send to apply them.~~

	==~~Example~~==

	In the example below, we have painted a dataset with 4 classes. Such dataset is great for demonstrating k-means and hierarchical clustering methods. In the screenshot, we see that k-Means, overall, recognizes clusters better than Hierarchical Clustering. It returns a score rank, where the best score (the one with the highest value) means the most likely number of clusters. Hierarchical clustering, however, doesn’t group the right classes together. This is a great tool for learning and exploring statistical concepts.		In the example below, we have painted a dataset with 4 classes. Such dataset is great for demonstrating k-means and hierarchical clustering methods. In the screenshot, we see that k-Means, overall, recognizes clusters better than Hierarchical Clustering. It returns a score rank, where the best score (the one with the highest value) means the most likely number of clusters. Hierarchical clustering, however, doesn’t group the right classes together. This is a great tool for learning and exploring statistical concepts.

Onnowpurbo at 04:38, 21 January 2020

2020-01-21T04:38:51Z

← Older revision		Revision as of 04:38, 21 January 2020
Line 10:		Line 10:
	The widget supports the creation of a new dataset by visually placing data points on a two-dimension plane. Data points can be placed on the plane individually (Put) or in a larger number by brushing (Brush). Data points can belong to classes if the data is intended to be used in supervised learning.		The widget supports the creation of a new dataset by visually placing data points on a two-dimension plane. Data points can be placed on the plane individually (Put) or in a larger number by brushing (Brush). Data points can belong to classes if the data is intended to be used in supervised learning.

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

	Name the axes and select a class to paint data instances. You can add or remove classes. Use only one class to create classless, unsupervised datasets.		Name the axes and select a class to paint data instances. You can add or remove classes. Use only one class to create classless, unsupervised datasets.
Line 24:		Line 24:
	Tick the box on the left to automatically commit changes to other widgets. Alternatively, press Send to apply them.		Tick the box on the left to automatically commit changes to other widgets. Alternatively, press Send to apply them.

	Example		==Example==

	In the example below, we have painted a dataset with 4 classes. Such dataset is great for demonstrating k-means and hierarchical clustering methods. In the screenshot, we see that k-Means, overall, recognizes clusters better than Hierarchical Clustering. It returns a score rank, where the best score (the one with the highest value) means the most likely number of clusters. Hierarchical clustering, however, doesn’t group the right classes together. This is a great tool for learning and exploring statistical concepts.		In the example below, we have painted a dataset with 4 classes. Such dataset is great for demonstrating k-means and hierarchical clustering methods. In the screenshot, we see that k-Means, overall, recognizes clusters better than Hierarchical Clustering. It returns a score rank, where the best score (the one with the highest value) means the most likely number of clusters. Hierarchical clustering, however, doesn’t group the right classes together. This is a great tool for learning and exploring statistical concepts.

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

Onnowpurbo: Created page with "Sumber: https://docs.biolab.si//3/visual-programming/widgets/data/paintdata.html Paints data on a 2D plane. You can place individual data points or use a brush to paint larg..."

2020-01-10T22:17:29Z

Created page with "Sumber: https://docs.biolab.si//3/visual-programming/widgets/data/paintdata.html Paints data on a 2D plane. You can place individual data points or use a brush to paint larg..."

New page

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

Paints data on a 2D plane. You can place individual data points or use a brush to paint larger datasets.

Outputs

Data: dataset as painted in the plot

The widget supports the creation of a new dataset by visually placing data points on a two-dimension plane. Data points can be placed on the plane individually (Put) or in a larger number by brushing (Brush). Data points can belong to classes if the data is intended to be used in supervised learning.

../../_images/PaintData-stamped.png

Name the axes and select a class to paint data instances. You can add or remove classes. Use only one class to create classless, unsupervised datasets.

Drawing tools. Paint data points with Brush (multiple data instances) or Put (individual data instance). Select data points with Select and remove them with the Delete/Backspace key. Reposition data points with Jitter (spread) and Magnet (focus). Use Zoom and scroll to zoom in or out. Below, set the radius and intensity for Brush, Put, Jitter and Magnet tools.

Reset to Input Data.

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

Produce a report.

Tick the box on the left to automatically commit changes to other widgets. Alternatively, press Send to apply them.

Example

In the example below, we have painted a dataset with 4 classes. Such dataset is great for demonstrating k-means and hierarchical clustering methods. In the screenshot, we see that k-Means, overall, recognizes clusters better than Hierarchical Clustering. It returns a score rank, where the best score (the one with the highest value) means the most likely number of clusters. Hierarchical clustering, however, doesn’t group the right classes together. This is a great tool for learning and exploring statistical concepts.

../../_images/PaintData-Example.png

==Referensi==

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

==Pranala Menarik==

* [[Orange]]