Orange: PCA - Revision history

Onnowpurbo: /* Output */

2020-02-22T21:13:05Z

Output

← Older revision		Revision as of 21:13, 22 February 2020
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	* Principal components graph, where the red (lower) line is the variance covered per component and the green (upper) line is cumulative variance covered by components.		* Principal components graph, where the red (lower) line is the variance covered per component and the green (upper) line is cumulative variance covered by components.

	~~The number of components of the transformation can be selected either in the~~ Components Selection input box ~~or by dragging the~~ vertical cutoff line ~~in the graph~~.		Jumlah komponen transformasi dapat dipilih dari Components Selection input box atau dengan drag vertical cutoff line di grafik.

	==Contoh==		==Contoh==

Onnowpurbo at 21:11, 22 February 2020

2020-02-22T21:11:30Z

← Older revision		Revision as of 21:11, 22 February 2020
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	~~Transformasi linear~~ PCA ~~dari~~ input data.		PCA mentransformasikan input data.

	==Input==		==Input==
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	==Contoh==		==Contoh==

	PCA ~~can be used to simplify visualizations of large datasets~~. ~~Below~~, ~~we used the~~ Iris dataset ~~to show how we can improve the visualization of the dataset with~~ PCA. ~~The transformed data in the~~ Scatter Plot ~~show a much clearer distinction between classes than the~~ default ~~settings~~.		PCA dapat digunakan untuk menyederhanakan visualisasi dataset yang besar. Di bawah ini, kita menggunakan dataset Iris untuk menunjukkan bagaimana kita dapat meningkatkan visualisasi dataset dengan PCA. Data yang diubah dalam Scatter Plot menunjukkan perbedaan yang jauh lebih jelas antara kelas daripada pengaturan default.

	[[File:PCAExample.png\|center\|200px\|thumb]]		[[File:PCAExample.png\|center\|200px\|thumb]]

	~~The widget provides two outputs~~: transformed data ~~and~~ principal ~~components~~. Transformed ~~data are weights for~~ individual ~~instances in the new coordinate system~~, ~~while components are the system descriptors~~ (~~weights for~~ principal ~~components~~). ~~When fed into the~~ Data Table, ~~we can see both outputs in numerical form~~. ~~We used two~~ data ~~tables in order to provide a more clean visualization of the workflow~~, ~~but you can also choose to edit the links in such a way that you display the~~ data ~~in just one~~ data ~~table~~. ~~You only need to create two links and connect the Transformed~~ data ~~and Components inputs to the Data~~ output.		Widget ini menyediakan dua output: transformed data dan principal component. Transformed Data adalah weight untuk individual instance dalam sistem koordinat baru, sedangkan component adalah deskriptor sistem (weight untuk principal component). Saat dimasukkan ke dalam Data Table, kita bisa melihat kedua output dalam bentuk angka. Kita menggunakan dua tabel data untuk menyediakan visualisasi workflowa yang lebih bersih, tetapi kita juga dapat memilih untuk mengedit link sedemikian rupa sehingga kita menampilkan data hanya dalam satu tabel data. Kita hanya perlu membuat dua link dan menghubungkan input data dan komponen yang ditransformasikan ke output data.

	[[File:PCAExample2.png\|center\|200px\|thumb]]		[[File:PCAExample2.png\|center\|200px\|thumb]]

Onnowpurbo at 04:54, 22 February 2020

2020-02-22T04:54:42Z

← Older revision		Revision as of 04:54, 22 February 2020
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	PCA ~~linear transformation of~~ input data.		Transformasi linear PCA dari input data.

	==Input==		==Input==
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	Components: Eigenvectors.		Components: Eigenvectors.

	Principal Component Analysis (PCA) ~~computes the~~ PCA linear transformation ~~of the~~ input data. ~~It outputs either a~~ transformed dataset ~~with weights of~~ individual ~~instances or weights of~~ principal components.		Principal Component Analysis (PCA) menghitung PCA linear transformation dari input data. Dia mengeluarkan sebuah transformed dataset dengan weight dari individual instance atau weight dari principal components.

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

Onnowpurbo at 01:06, 29 January 2020

2020-01-29T01:06:21Z

← Older revision		Revision as of 01:06, 29 January 2020
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	PCA linear transformation of input data.		PCA linear transformation of input data.

	~~Inputs~~		==Input==

	Data: input dataset		Data: input dataset

	~~Outputs~~		==Output==

	Transformed Data: PCA transformed data		Transformed Data: PCA transformed data
	Components: Eigenvectors.		Components: Eigenvectors.

	Principal Component Analysis (PCA) computes the PCA linear transformation of the input data. It outputs either a transformed dataset with weights of individual instances or weights of principal components.		Principal Component Analysis (PCA) computes the PCA linear transformation of the input data. It outputs either a transformed dataset with weights of individual instances or weights of principal components.
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	Select how many principal components you wish in your output. It is best to choose as few as possible with variance covered as high as possible. You can also set how much variance you wish to cover with your principal components.		* Select how many principal components you wish in your output. It is best to choose as few as possible with variance covered as high as possible. You can also set how much variance you wish to cover with your principal components.
	You can normalize data to adjust the values to common scale.		* You can normalize data to adjust the values to common scale.
	When Apply Automatically is ticked, the widget will automatically communicate all changes. Alternatively, click Apply.		* When Apply Automatically is ticked, the widget will automatically communicate all changes. Alternatively, click Apply.
	Press Save Image if you want to save the created image to your computer.		* Press Save Image if you want to save the created image to your computer.
	Produce a report.		* Produce a report.
	Principal components graph, where the red (lower) line is the variance covered per component and the green (upper) line is cumulative variance covered by components.		* Principal components graph, where the red (lower) line is the variance covered per component and the green (upper) line is cumulative variance covered by components.

	The number of components of the transformation can be selected either in the Components Selection input box or by dragging the vertical cutoff line in the graph.		The number of components of the transformation can be selected either in the Components Selection input box or by dragging the vertical cutoff line in the graph.

Onnowpurbo at 00:39, 24 January 2020

2020-01-24T00:39:24Z

← Older revision		Revision as of 00:39, 24 January 2020
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	Principal Component Analysis (PCA) computes the PCA linear transformation of the input data. It outputs either a transformed dataset with weights of individual instances or weights of principal components.		Principal Component Analysis (PCA) computes the PCA linear transformation of the input data. It outputs either a transformed dataset with weights of individual instances or weights of principal components.

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

	~~../../_images/PCA-stamped.png~~

	Select how many principal components you wish in your output. It is best to choose as few as possible with variance covered as high as possible. You can also set how much variance you wish to cover with your principal components.		Select how many principal components you wish in your output. It is best to choose as few as possible with variance covered as high as possible. You can also set how much variance you wish to cover with your principal components.
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	PCA can be used to simplify visualizations of large datasets. Below, we used the Iris dataset to show how we can improve the visualization of the dataset with PCA. The transformed data in the Scatter Plot show a much clearer distinction between classes than the default settings.		PCA can be used to simplify visualizations of large datasets. Below, we used the Iris dataset to show how we can improve the visualization of the dataset with PCA. The transformed data in the Scatter Plot show a much clearer distinction between classes than the default settings.

	~~../../_images/~~PCAExample.png		[[File:PCAExample.png\|center\|200px\|thumb]]

	The widget provides two outputs: transformed data and principal components. Transformed data are weights for individual instances in the new coordinate system, while components are the system descriptors (weights for principal components). When fed into the Data Table, we can see both outputs in numerical form. We used two data tables in order to provide a more clean visualization of the workflow, but you can also choose to edit the links in such a way that you display the data in just one data table. You only need to create two links and connect the Transformed data and Components inputs to the Data output.		The widget provides two outputs: transformed data and principal components. Transformed data are weights for individual instances in the new coordinate system, while components are the system descriptors (weights for principal components). When fed into the Data Table, we can see both outputs in numerical form. We used two data tables in order to provide a more clean visualization of the workflow, but you can also choose to edit the links in such a way that you display the data in just one data table. You only need to create two links and connect the Transformed data and Components inputs to the Data output.

	~~../../_images/~~PCAExample2.png		[[File:PCAExample2.png\|center\|200px\|thumb]]

Onnowpurbo at 06:08, 23 January 2020

2020-01-23T06:08:24Z

← Older revision		Revision as of 06:08, 23 January 2020
Line 11:		Line 11:

	Transformed Data: PCA transformed data		Transformed Data: PCA transformed data

	Components: Eigenvectors.		Components: Eigenvectors.

	Principal Component Analysis (PCA) computes the PCA linear transformation of the input data. It outputs either a transformed dataset with weights of individual instances or weights of principal components.		Principal Component Analysis (PCA) computes the PCA linear transformation of the input data. It outputs either a transformed dataset with weights of individual instances or weights of principal components.


	../../_images/PCA-stamped.png		../../_images/PCA-stamped.png

	Select how many principal components you wish in your output. It is best to choose as few as possible with variance covered as high as possible. You can also set how much variance you wish to cover with your principal components.		Select how many principal components you wish in your output. It is best to choose as few as possible with variance covered as high as possible. You can also set how much variance you wish to cover with your principal components.

	You can normalize data to adjust the values to common scale.		You can normalize data to adjust the values to common scale.

	When Apply Automatically is ticked, the widget will automatically communicate all changes. Alternatively, click Apply.		When Apply Automatically is ticked, the widget will automatically communicate all changes. Alternatively, click Apply.

	Press Save Image if you want to save the created image to your computer.		Press Save Image if you want to save the created image to your computer.

	Produce a report.		Produce a report.

	Principal components graph, where the red (lower) line is the variance covered per component and the green (upper) line is cumulative variance covered by components.		Principal components graph, where the red (lower) line is the variance covered per component and the green (upper) line is cumulative variance covered by components.

	The number of components of the transformation can be selected either in the Components Selection input box or by dragging the vertical cutoff line in the graph.		The number of components of the transformation can be selected either in the Components Selection input box or by dragging the vertical cutoff line in the graph.
	~~Examples~~
			==Contoh==

	PCA can be used to simplify visualizations of large datasets. Below, we used the Iris dataset to show how we can improve the visualization of the dataset with PCA. The transformed data in the Scatter Plot show a much clearer distinction between classes than the default settings.		PCA can be used to simplify visualizations of large datasets. Below, we used the Iris dataset to show how we can improve the visualization of the dataset with PCA. The transformed data in the Scatter Plot show a much clearer distinction between classes than the default settings.

Onnowpurbo: Created page with "Sumber: https://docs.biolab.si//3/visual-programming/widgets/unsupervised/PCA.html PCA linear transformation of input data. Inputs Data: input dataset Outputs Tr..."

2020-01-16T23:49:36Z

Created page with "Sumber: https://docs.biolab.si//3/visual-programming/widgets/unsupervised/PCA.html PCA linear transformation of input data. Inputs Data: input dataset Outputs Tr..."

New page

Sumber: https://docs.biolab.si//3/visual-programming/widgets/unsupervised/PCA.html

PCA linear transformation of input data.

Inputs

Data: input dataset

Outputs

Transformed Data: PCA transformed data

Components: Eigenvectors.

Principal Component Analysis (PCA) computes the PCA linear transformation of the input data. It outputs either a transformed dataset with weights of individual instances or weights of principal components.

../../_images/PCA-stamped.png

Select how many principal components you wish in your output. It is best to choose as few as possible with variance covered as high as possible. You can also set how much variance you wish to cover with your principal components.

You can normalize data to adjust the values to common scale.

When Apply Automatically is ticked, the widget will automatically communicate all changes. Alternatively, click Apply.

Press Save Image if you want to save the created image to your computer.

Produce a report.

Principal components graph, where the red (lower) line is the variance covered per component and the green (upper) line is cumulative variance covered by components.

The number of components of the transformation can be selected either in the Components Selection input box or by dragging the vertical cutoff line in the graph.
Examples

PCA can be used to simplify visualizations of large datasets. Below, we used the Iris dataset to show how we can improve the visualization of the dataset with PCA. The transformed data in the Scatter Plot show a much clearer distinction between classes than the default settings.

../../_images/PCAExample.png

The widget provides two outputs: transformed data and principal components. Transformed data are weights for individual instances in the new coordinate system, while components are the system descriptors (weights for principal components). When fed into the Data Table, we can see both outputs in numerical form. We used two data tables in order to provide a more clean visualization of the workflow, but you can also choose to edit the links in such a way that you display the data in just one data table. You only need to create two links and connect the Transformed data and Components inputs to the Data output.

../../_images/PCAExample2.png

==Referensi==

* https://docs.biolab.si//3/visual-programming/widgets/unsupervised/PCA.html

==Pranala Menarik==

* [[Orange]]