Orange: kNN - Revision history

Onnowpurbo: /* Referensi */

2020-04-10T22:55:13Z

Referensi

← Older revision		Revision as of 22:55, 10 April 2020
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	[[File:KNN-regression.png\|center\|600px\|thumb]]		[[File:KNN-regression.png\|center\|600px\|thumb]]

			==Youtube==

			* [https://www.youtube.com/watch?v=vi0kMTmVL_E YOUTUBE: ORANGE model kNN]

	==Referensi==		==Referensi==

	* https://docs.orange.biolab.si/3/visual-programming/widgets/model/knn.html		* https://docs.orange.biolab.si/3/visual-programming/widgets/model/knn.html


	==Pranala Menarik==		==Pranala Menarik==

	* [[Orange]]		* [[Orange]]

Onnowpurbo: /* Contoh */

2020-04-10T22:31:06Z

Contoh

← Older revision		Revision as of 22:31, 10 April 2020
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	Contoh pertama adalah task klasifikasi pada dataset iris. Kita bandingkan hasil dari k-Nearest neighbors dengan default model Constant, yang akan memprediksi class majoritas.		Contoh pertama adalah task klasifikasi pada dataset iris. Kita bandingkan hasil dari k-Nearest neighbors dengan default model Constant, yang akan memprediksi class majoritas.

	[[File:Constant-classification.png\|center\|~~200px~~\|thumb]]		[[File:Constant-classification.png\|center\|600px\|thumb]]

	Contoh kedua adalah task regresi. Workflow ini menunjukkan cara menggunakan Learner output. Untuk tujuan contoh ini, kita menggunakan dataset housing. Kita memasukkan model prediksi kNN ke dalam Predictions dan mengamati nilai yang diprediksi.		Contoh kedua adalah task regresi. Workflow ini menunjukkan cara menggunakan Learner output. Untuk tujuan contoh ini, kita menggunakan dataset housing. Kita memasukkan model prediksi kNN ke dalam Predictions dan mengamati nilai yang diprediksi.

	[[File:KNN-regression.png\|center\|~~200px~~\|thumb]]		[[File:KNN-regression.png\|center\|600px\|thumb]]

	==Referensi==		==Referensi==

Onnowpurbo: /* Contoh */

2020-03-03T21:39:14Z

Contoh

← Older revision		Revision as of 21:39, 3 March 2020
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	==Contoh==		==Contoh==

	Contoh pertama adalah ~~tugas~~ klasifikasi pada dataset iris. Kita bandingkan hasil dari k-Nearest neighbors dengan default model Constant, yang akan memprediksi class majoritas.		Contoh pertama adalah task klasifikasi pada dataset iris. Kita bandingkan hasil dari k-Nearest neighbors dengan default model Constant, yang akan memprediksi class majoritas.

	[[File:Constant-classification.png\|center\|200px\|thumb]]		[[File:Constant-classification.png\|center\|200px\|thumb]]

	~~The second example is a regression~~ task. ~~This workflow shows how to use the~~ Learner output. ~~For the purpose of this example~~, ~~we used the~~ housing ~~dataset~~. ~~We input the~~ kNN ~~prediction model into~~ Predictions ~~and observe the predicted values~~.		Contoh kedua adalah task regresi. Workflow ini menunjukkan cara menggunakan Learner output. Untuk tujuan contoh ini, kita menggunakan dataset housing. Kita memasukkan model prediksi kNN ke dalam Predictions dan mengamati nilai yang diprediksi.

	[[File:KNN-regression.png\|center\|200px\|thumb]]		[[File:KNN-regression.png\|center\|200px\|thumb]]





	==Referensi==		==Referensi==

Onnowpurbo: /* Output */

2020-03-03T21:37:26Z

Output

← Older revision		Revision as of 21:37, 3 March 2020
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	Model: trained model		Model: trained model

	Widget kNN menggunakan algoritma kNN yang akan mencari k ~~contoh~~ training terdekat di ruang feature dan menggunakan rata-rata feature terdekat tersebut untuk mem-prediksi.		Widget kNN menggunakan algoritma kNN yang akan mencari k instance training terdekat di ruang feature dan menggunakan rata-rata feature terdekat tersebut untuk mem-prediksi.

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

Onnowpurbo at 10:58, 2 March 2020

2020-03-02T10:58:30Z

← Older revision		Revision as of 10:58, 2 March 2020
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	sumber: https://docs.orange.biolab.si/3/visual-programming/widgets/model/knn.html		sumber: https://docs.orange.biolab.si/3/visual-programming/widgets/model/knn.html

	~~Predict according to the nearest~~ training ~~instances~~.		Prediksi berdasarkan instance training terdekat.

	==Input==		==Input==
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	Model: trained model		Model: trained model

	~~The~~ kNN ~~widget uses the~~ kNN ~~algorithm that searches for~~ k ~~closest~~ training ~~examples in~~ feature ~~space and uses their average as prediction~~.		Widget kNN menggunakan algoritma kNN yang akan mencari k contoh training terdekat di ruang feature dan menggunakan rata-rata feature terdekat tersebut untuk mem-prediksi.

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

	~~The first example is a classification task on~~ iris ~~dataset~~. ~~We compare the results of~~ k-Nearest neighbors ~~with the~~ default model Constant, ~~which always predicts the majority~~ class.		Contoh pertama adalah tugas klasifikasi pada dataset iris. Kita bandingkan hasil dari k-Nearest neighbors dengan default model Constant, yang akan memprediksi class majoritas.

	[[File:Constant-classification.png\|center\|200px\|thumb]]		[[File:Constant-classification.png\|center\|200px\|thumb]]

Onnowpurbo at 03:35, 28 January 2020

2020-01-28T03:35:00Z

← Older revision		Revision as of 03:35, 28 January 2020
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	Predict according to the nearest training instances.		Predict according to the nearest training instances.

	~~Inputs~~		==Input==

	Data: input dataset		Data: input dataset
	Preprocessor: preprocessing method(s)		Preprocessor: preprocessing method(s)

	~~Outputs~~		==Output==

	Learner: kNN learning algorithm		Learner: kNN learning algorithm
	Model: trained model		Model: trained model

	The kNN widget uses the kNN algorithm that searches for k closest training examples in feature space and uses their average as prediction.		The kNN widget uses the kNN algorithm that searches for k closest training examples in feature space and uses their average as prediction.
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	[[File:KNN-stamped.png\|center\|200px\|thumb]]		[[File:KNN-stamped.png\|center\|200px\|thumb]]

	A name under which it will appear in other widgets. The default name is “kNN”.		* A name under which it will appear in other widgets. The default name is “kNN”.
	Set the number of nearest neighbors, the distance parameter (metric) and weights as model criteria.		* Set the number of nearest neighbors, the distance parameter (metric) and weights as model criteria.

	Metric can be:		** Metric can be:
	Euclidean (“straight line”, distance between two points)		*** Euclidean (“straight line”, distance between two points)
	Manhattan (sum of absolute differences of all attributes)		*** Manhattan (sum of absolute differences of all attributes)
	Maximal (greatest of absolute differences between attributes)		*** Maximal (greatest of absolute differences between attributes)
	Mahalanobis (distance between point and distribution).		*** Mahalanobis (distance between point and distribution).

	The Weights you can use are:		** The Weights you can use are:
	Uniform: all points in each neighborhood are weighted equally.		*** Uniform: all points in each neighborhood are weighted equally.
	Distance: closer neighbors of a query point have a greater influence than the neighbors further away.		*** Distance: closer neighbors of a query point have a greater influence than the neighbors further away.
	~~Produce a report~~.

	When you change one or more settings, you need to click Apply, which will put a new learner on the output and, if the training examples are given, construct a new model and output it as well. Changes can also be applied automatically by clicking the box on the left side of the Apply button.		* Produce a report.
			* When you change one or more settings, you need to click Apply, which will put a new learner on the output and, if the training examples are given, construct a new model and output it as well. Changes can also be applied automatically by clicking the box on the left side of the Apply button.

	==Contoh==		==Contoh==

Onnowpurbo at 02:04, 23 January 2020

2020-01-23T02:04:55Z

← Older revision		Revision as of 02:04, 23 January 2020
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	Data: input dataset		Data: input dataset

	Preprocessor: preprocessing method(s)		Preprocessor: preprocessing method(s)

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	Learner: kNN learning algorithm		Learner: kNN learning algorithm

	Model: trained model		Model: trained model

	The kNN widget uses the kNN algorithm that searches for k closest training examples in feature space and uses their average as prediction.		The kNN widget uses the kNN algorithm that searches for k closest training examples in feature space and uses their average as prediction.

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

	A name under which it will appear in other widgets. The default name is “kNN”.		A name under which it will appear in other widgets. The default name is “kNN”.

	Set the number of nearest neighbors, the distance parameter (metric) and weights as model criteria.		Set the number of nearest neighbors, the distance parameter (metric) and weights as model criteria.

	Metric can be:		Metric can be:

	Euclidean (“straight line”, distance between two points)		Euclidean (“straight line”, distance between two points)

	Manhattan (sum of absolute differences of all attributes)		Manhattan (sum of absolute differences of all attributes)

	Maximal (greatest of absolute differences between attributes)		Maximal (greatest of absolute differences between attributes)

	Mahalanobis (distance between point and distribution).		Mahalanobis (distance between point and distribution).

	The Weights you can use are:		The Weights you can use are:

	Uniform: all points in each neighborhood are weighted equally.		Uniform: all points in each neighborhood are weighted equally.

	Distance: closer neighbors of a query point have a greater influence than the neighbors further away.		Distance: closer neighbors of a query point have a greater influence than the neighbors further away.

	Produce a report.		Produce a report.

	When you change one or more settings, you need to click Apply, which will put a new learner on the output and, if the training examples are given, construct a new model and output it as well. Changes can also be applied automatically by clicking the box on the left side of the Apply button.		When you change one or more settings, you need to click Apply, which will put a new learner on the output and, if the training examples are given, construct a new model and output it as well. Changes can also be applied automatically by clicking the box on the left side of the Apply button.

	~~Examples~~		==Contoh==

	The first example is a classification task on iris dataset. We compare the results of k-Nearest neighbors with the default model Constant, which always predicts the majority class.		The first example is a classification task on iris dataset. We compare the results of k-Nearest neighbors with the default model Constant, which always predicts the majority class.

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

	The second example is a regression task. This workflow shows how to use the Learner output. For the purpose of this example, we used the housing dataset. We input the kNN prediction model into Predictions and observe the predicted values.		The second example is a regression task. This workflow shows how to use the Learner output. For the purpose of this example, we used the housing dataset. We input the kNN prediction model into Predictions and observe the predicted values.

	~~../../_images/kNN~~-regression.png		[[File:KNN-regression.png\|center\|200px\|thumb]]

Onnowpurbo at 13:33, 12 January 2020

2020-01-12T13:33:36Z

← Older revision		Revision as of 13:33, 12 January 2020
Line 1:		Line 1:
	sumber: https://docs.orange.biolab.si/3/visual-programming/widgets/model/knn.html		sumber: https://docs.orange.biolab.si/3/visual-programming/widgets/model/knn.html

			Predict according to the nearest training instances.

			Inputs

			Data: input dataset

			Preprocessor: preprocessing method(s)

			Outputs

			Learner: kNN learning algorithm

			Model: trained model

			The kNN widget uses the kNN algorithm that searches for k closest training examples in feature space and uses their average as prediction.

			../../_images/kNN-stamped.png

			A name under which it will appear in other widgets. The default name is “kNN”.

			Set the number of nearest neighbors, the distance parameter (metric) and weights as model criteria.

			Metric can be:

			Euclidean (“straight line”, distance between two points)

			Manhattan (sum of absolute differences of all attributes)

			Maximal (greatest of absolute differences between attributes)

			Mahalanobis (distance between point and distribution).

			The Weights you can use are:

			Uniform: all points in each neighborhood are weighted equally.

			Distance: closer neighbors of a query point have a greater influence than the neighbors further away.

			Produce a report.

			When you change one or more settings, you need to click Apply, which will put a new learner on the output and, if the training examples are given, construct a new model and output it as well. Changes can also be applied automatically by clicking the box on the left side of the Apply button.

			Examples

			The first example is a classification task on iris dataset. We compare the results of k-Nearest neighbors with the default model Constant, which always predicts the majority class.

			../../_images/Constant-classification.png

			The second example is a regression task. This workflow shows how to use the Learner output. For the purpose of this example, we used the housing dataset. We input the kNN prediction model into Predictions and observe the predicted values.

			../../_images/kNN-regression.png




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	* https://docs.orange.biolab.si/3/visual-programming/widgets/model/knn.html		* https://docs.orange.biolab.si/3/visual-programming/widgets/model/knn.html


			==Pranala Menarik==

			* [[Orange]]

Onnowpurbo: Created page with "sumber: https://docs.orange.biolab.si/3/visual-programming/widgets/model/knn.html ==Referensi== * https://docs.orange.biolab.si/3/visual-programming/widgets/model/knn.html"

2017-07-30T04:34:02Z

Created page with "sumber: https://docs.orange.biolab.si/3/visual-programming/widgets/model/knn.html ==Referensi== * https://docs.orange.biolab.si/3/visual-programming/widgets/model/knn.html"

New page

sumber: https://docs.orange.biolab.si/3/visual-programming/widgets/model/knn.html

==Referensi==

* https://docs.orange.biolab.si/3/visual-programming/widgets/model/knn.html