Orange: kNN - Revision history

Onnowpurbo: /* Referensi */

2020-04-10T22:55:13Z

Referensi

Onnowpurbo: /* Contoh */

2020-04-10T22:31:06Z

Contoh

Onnowpurbo: /* Contoh */

2020-03-03T21:39:14Z

Contoh

Onnowpurbo: /* Output */

2020-03-03T21:37:26Z

Output

Onnowpurbo at 10:58, 2 March 2020

2020-03-02T10:58:30Z

Onnowpurbo at 03:35, 28 January 2020

2020-01-28T03:35:00Z

Onnowpurbo at 02:04, 23 January 2020

2020-01-23T02:04:55Z

Onnowpurbo at 13:33, 12 January 2020

2020-01-12T13:33:36Z

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

@@ Line 42: / Line 42: @@
 [[File:KNN-regression.png|center|600px|thumb]]
 ==Referensi==
 * https://docs.orange.biolab.si/3/visual-programming/widgets/model/knn.html
 ==Pranala Menarik==
 * [[Orange]]

@@ Line 37: / Line 37: @@
 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.
-[[File:KNN-regression.png|center|200px|thumb]]
+[[File:KNN-regression.png|center|600px|thumb]]
 ==Referensi==

@@ Line 35: / Line 35: @@
 ==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]]
-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]]
 ==Referensi==

@@ Line 13: / Line 13: @@
   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]]

@@ Line 1: / Line 1: @@
 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==
@@ Line 13: / Line 13: @@
   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]]
@@ Line 35: / Line 35: @@
 ==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]]

@@ Line 3: / Line 3: @@
 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.
@@ Line 17: / Line 17: @@
 [[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.
-    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.
 ==Contoh==

← 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]]