Orange: Outliers - Revision history

Onnowpurbo at 07:00, 18 April 2020

2020-04-18T07:00:29Z

← Older revision		Revision as of 07:00, 18 April 2020
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	Sumber: https://docs.biolab.si//3/visual-programming/widgets/data/outliers.html		Sumber: https://docs.biolab.si//3/visual-programming/widgets/data/outliers.html

	Widget Outliers mendeteksi outlier sederhana dengan membandingkan jarak antar instance.		Widget [[Outliers]] mendeteksi outlier sederhana dengan membandingkan jarak antar instance.

	==Input==		==Input==
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	Inliers: instances not scored as outliers		Inliers: instances not scored as outliers

	Widget Outliers menerapkan salah satu dari dua metode untuk mendeteksi outlier. Kedua metode menerapkan klasifikasi ke dataset, satu dengan SVM (multiple kernel) dan yang lainnya dengan elliptical envelope. SVM satu kelas dengan non-linear kernel (RBF) berkinerja baik untuk distribusi non-Gaussian, sedangkan estimator Covariance hanya berfungsi untuk data dengan distribusi Gaussian.		Widget [[Outliers]] menerapkan salah satu dari dua metode untuk mendeteksi outlier. Kedua metode menerapkan klasifikasi ke dataset, satu dengan SVM (multiple kernel) dan yang lainnya dengan elliptical envelope. SVM satu kelas dengan non-linear kernel (RBF) berkinerja baik untuk distribusi non-Gaussian, sedangkan estimator Covariance hanya berfungsi untuk data dengan distribusi Gaussian.

	[[File:Outliers-stamped.png\|center\|400px\|thumb]]		[[File:Outliers-stamped.png\|center\|400px\|thumb]]
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	==Contoh==		==Contoh==

	Di bawah, adalah contoh sederhana cara menggunakan widget Outliers. Kita menggunakan dataset Iris untuk mendeteksi outlier. Kita memilih satu kelas SVM dengan metode non-linear kernel(RBF), dengan Nu ditetapkan pada 20% (lebih sedikit kesalahan pelatihan, lebih banyak vektor dukungan). Kemudian kita mengamati ~~outliers~~ di widget Data Table, sementara kita mengirim inliers ke widget Scatter Plot.		Di bawah, adalah contoh sederhana cara menggunakan widget Outliers. Kita menggunakan dataset Iris untuk mendeteksi outlier. Kita memilih satu kelas SVM dengan metode non-linear kernel(RBF), dengan Nu ditetapkan pada 20% (lebih sedikit kesalahan pelatihan, lebih banyak vektor dukungan). Kemudian kita mengamati [[Outliers]] di widget Data Table, sementara kita mengirim inliers ke widget Scatter Plot.

	[[File:Outliers-Example.png\|center\|600px\|thumb]]		[[File:Outliers-Example.png\|center\|600px\|thumb]]

Onnowpurbo at 06:59, 18 April 2020

2020-04-18T06:59:05Z

← Older revision		Revision as of 06:59, 18 April 2020
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	Sumber: https://docs.biolab.si//3/visual-programming/widgets/data/outliers.html		Sumber: https://docs.biolab.si//3/visual-programming/widgets/data/outliers.html

	~~Deteksi~~ outlier sederhana dengan membandingkan jarak antar instance.		Widget Outliers mendeteksi outlier sederhana dengan membandingkan jarak antar instance.

	==Input==		==Input==
Line 13:		Line 13:
	Inliers: instances not scored as outliers		Inliers: instances not scored as outliers

	Outliers ~~widget~~ menerapkan salah satu dari dua metode untuk ~~deteksi~~ outlier. Kedua metode menerapkan klasifikasi ke dataset, satu dengan SVM (multiple kernel) dan yang lainnya dengan elliptical envelope. SVM satu kelas dengan non-linear kernel (RBF) berkinerja baik ~~dengan~~ distribusi non-Gaussian, sedangkan estimator Covariance hanya berfungsi untuk data dengan distribusi Gaussian.		Widget Outliers menerapkan salah satu dari dua metode untuk mendeteksi outlier. Kedua metode menerapkan klasifikasi ke dataset, satu dengan SVM (multiple kernel) dan yang lainnya dengan elliptical envelope. SVM satu kelas dengan non-linear kernel (RBF) berkinerja baik untuk distribusi non-Gaussian, sedangkan estimator Covariance hanya berfungsi untuk data dengan distribusi Gaussian.

	[[File:Outliers-stamped.png\|center\|~~200px~~\|thumb]]		[[File:Outliers-stamped.png\|center\|400px\|thumb]]


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	==Contoh==		==Contoh==

	Di bawah, adalah contoh sederhana cara menggunakan widget ~~ini~~. Kita menggunakan dataset Iris untuk mendeteksi outlier. Kita memilih satu kelas SVM dengan metode non-linear kernel(RBF), dengan Nu ditetapkan pada 20% (lebih sedikit kesalahan pelatihan, lebih banyak vektor dukungan). Kemudian kita mengamati outliers di Data Table ~~widget~~, sementara kita mengirim inliers ke Scatter Plot.		Di bawah, adalah contoh sederhana cara menggunakan widget Outliers. Kita menggunakan dataset Iris untuk mendeteksi outlier. Kita memilih satu kelas SVM dengan metode non-linear kernel(RBF), dengan Nu ditetapkan pada 20% (lebih sedikit kesalahan pelatihan, lebih banyak vektor dukungan). Kemudian kita mengamati outliers di widget Data Table, sementara kita mengirim inliers ke widget Scatter Plot.

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

Onnowpurbo at 01:48, 31 January 2020

2020-01-31T01:48:01Z

← Older revision		Revision as of 01:48, 31 January 2020
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	Sumber: https://docs.biolab.si//3/visual-programming/widgets/data/outliers.html		Sumber: https://docs.biolab.si//3/visual-programming/widgets/data/outliers.html

			Deteksi outlier sederhana dengan membandingkan jarak antar instance.
	~~Simple~~ outlier ~~detection by comparing distances between instances~~.

	==Input==		==Input==
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	Inliers: instances not scored as outliers		Inliers: instances not scored as outliers

	~~The~~ Outliers widget ~~applies one of the two methods for~~ outlier ~~detection~~. ~~Both methods apply classification to the~~ dataset, ~~one with~~ SVM (multiple ~~kernels~~) ~~and the other with~~ elliptical envelope. ~~One-class~~ SVM ~~with~~ non-linear ~~kernels~~ (RBF) ~~performs well with~~ non-Gaussian ~~distributions~~, ~~while~~ Covariance ~~estimator works only for~~ data ~~with~~ Gaussian ~~distribution~~.		Outliers widget menerapkan salah satu dari dua metode untuk deteksi outlier. Kedua metode menerapkan klasifikasi ke dataset, satu dengan SVM (multiple kernel) dan yang lainnya dengan elliptical envelope. SVM satu kelas dengan non-linear kernel (RBF) berkinerja baik dengan distribusi non-Gaussian, sedangkan estimator Covariance hanya berfungsi untuk data dengan distribusi Gaussian.

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

	~~Below~~, ~~is a simple example of how to use this~~ widget. ~~We used the~~ Iris ~~dataset to detect the outliers~~. ~~We chose the one class~~ SVM ~~with~~ non-linear kernel (RBF) ~~method~~, ~~with~~ Nu ~~set at~~ 20% (~~less training errors~~, ~~more support vectors~~). ~~Then we observed the~~ outliers ~~in the~~ Data Table widget, ~~while we sent the~~ inliers ~~to the~~ Scatter Plot.		Di bawah, adalah contoh sederhana cara menggunakan widget ini. Kita menggunakan dataset Iris untuk mendeteksi outlier. Kita memilih satu kelas SVM dengan metode non-linear kernel(RBF), dengan Nu ditetapkan pada 20% (lebih sedikit kesalahan pelatihan, lebih banyak vektor dukungan). Kemudian kita mengamati outliers di Data Table widget, sementara kita mengirim inliers ke Scatter Plot.

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

Onnowpurbo at 06:25, 27 January 2020

2020-01-27T06:25:45Z

← Older revision		Revision as of 06:25, 27 January 2020
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	Simple outlier detection by comparing distances between instances.		Simple outlier detection by comparing distances between instances.

	~~Inputs~~		==Input==

	Data: input dataset		Data: input dataset
			Distances: distance matrix

	~~Distances: distance matrix~~		==Output==

	~~Outputs~~		Outliers: instances scored as outliers
			Inliers: instances not scored as outliers
	Outliers: instances scored as outliers

	Inliers: instances not scored as outliers

	The Outliers widget applies one of the two methods for outlier detection. Both methods apply classification to the dataset, one with SVM (multiple kernels) and the other with elliptical envelope. One-class SVM with non-linear kernels (RBF) performs well with non-Gaussian distributions, while Covariance estimator works only for data with Gaussian distribution.		The Outliers widget applies one of the two methods for outlier detection. Both methods apply classification to the dataset, one with SVM (multiple kernels) and the other with elliptical envelope. One-class SVM with non-linear kernels (RBF) performs well with non-Gaussian distributions, while Covariance estimator works only for data with Gaussian distribution.
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	Information on the input data, number of inliers and outliers based on the selected model.		* Information on the input data, number of inliers and outliers based on the selected model.
			* Select the Outlier detection method:
	Select the Outlier detection method:		** One class SVM with non-linear kernel (RBF): classifies data as similar or different from the core class:
			*** Nu is a parameter for the upper bound on the fraction of training errors and a lower bound of the fraction of support vectors
	One class SVM with non-linear kernel (RBF): classifies data as similar or different from the core class:		*** Kernel coefficient is a gamma parameter, which specifies how much influence a single data instance has
			** Covariance estimator: fits ellipsis to central points with Mahalanobis distance metric
	Nu is a parameter for the upper bound on the fraction of training errors and a lower bound of the fraction of support vectors		*** Contamination is the proportion of outliers in the dataset
			*** Support fraction specifies the proportion of points included in the estimate
	Kernel coefficient is a gamma parameter, which specifies how much influence a single data instance has		* Produce a report.
			* Click Detect outliers to output the data.
	Covariance estimator: fits ellipsis to central points with Mahalanobis distance metric

	Contamination is the proportion of outliers in the dataset

	Support fraction specifies the proportion of points included in the estimate

	Produce a report.

	Click Detect outliers to output the data.

	==Contoh==		==Contoh==

Onnowpurbo at 01:47, 22 January 2020

2020-01-22T01:47:40Z

← Older revision		Revision as of 01:47, 22 January 2020
Line 18:		Line 18:
	The Outliers widget applies one of the two methods for outlier detection. Both methods apply classification to the dataset, one with SVM (multiple kernels) and the other with elliptical envelope. One-class SVM with non-linear kernels (RBF) performs well with non-Gaussian distributions, while Covariance estimator works only for data with Gaussian distribution.		The Outliers widget applies one of the two methods for outlier detection. Both methods apply classification to the dataset, one with SVM (multiple kernels) and the other with elliptical envelope. One-class SVM with non-linear kernels (RBF) performs well with non-Gaussian distributions, while Covariance estimator works only for data with Gaussian distribution.

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


	Information on the input data, number of inliers and outliers based on the selected model.		Information on the input data, number of inliers and outliers based on the selected model.
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	Click Detect outliers to output the data.		Click Detect outliers to output the data.

	~~Example~~		==Contoh==

	Below, is a simple example of how to use this widget. We used the Iris dataset to detect the outliers. We chose the one class SVM with non-linear kernel (RBF) method, with Nu set at 20% (less training errors, more support vectors). Then we observed the outliers in the Data Table widget, while we sent the inliers to the Scatter Plot.		Below, is a simple example of how to use this widget. We used the Iris dataset to detect the outliers. We chose the one class SVM with non-linear kernel (RBF) method, with Nu set at 20% (less training errors, more support vectors). Then we observed the outliers in the Data Table widget, while we sent the inliers to the Scatter Plot.

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

Onnowpurbo: Created page with "Sumber: https://docs.biolab.si//3/visual-programming/widgets/data/outliers.html Simple outlier detection by comparing distances between instances. Inputs Data: input d..."

2020-01-11T00:40:16Z

Created page with "Sumber: https://docs.biolab.si//3/visual-programming/widgets/data/outliers.html Simple outlier detection by comparing distances between instances. Inputs Data: input d..."

New page

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

Simple outlier detection by comparing distances between instances.

Inputs

Data: input dataset

Distances: distance matrix

Outputs

Outliers: instances scored as outliers

Inliers: instances not scored as outliers

The Outliers widget applies one of the two methods for outlier detection. Both methods apply classification to the dataset, one with SVM (multiple kernels) and the other with elliptical envelope. One-class SVM with non-linear kernels (RBF) performs well with non-Gaussian distributions, while Covariance estimator works only for data with Gaussian distribution.

../../_images/Outliers-stamped.png

Information on the input data, number of inliers and outliers based on the selected model.

Select the Outlier detection method:

One class SVM with non-linear kernel (RBF): classifies data as similar or different from the core class:

Nu is a parameter for the upper bound on the fraction of training errors and a lower bound of the fraction of support vectors

Kernel coefficient is a gamma parameter, which specifies how much influence a single data instance has

Covariance estimator: fits ellipsis to central points with Mahalanobis distance metric

Contamination is the proportion of outliers in the dataset

Support fraction specifies the proportion of points included in the estimate

Produce a report.

Click Detect outliers to output the data.

Example

Below, is a simple example of how to use this widget. We used the Iris dataset to detect the outliers. We chose the one class SVM with non-linear kernel (RBF) method, with Nu set at 20% (less training errors, more support vectors). Then we observed the outliers in the Data Table widget, while we sent the inliers to the Scatter Plot.

../../_images/Outliers-Example.png

==Referensi==

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

==Pranala Menarik==

* [[Orange]]