Orange: Logistic Regression - Revision history

Onnowpurbo: /* Youtube */

2020-04-08T22:00:55Z

Youtube

Onnowpurbo: /* Referensi */

2020-04-08T21:59:58Z

Referensi

Onnowpurbo: /* Contoh */

2020-04-06T02:18:52Z

Contoh

Onnowpurbo at 10:26, 5 April 2020

2020-04-05T10:26:55Z

Onnowpurbo: /* Contoh */

2020-03-06T08:15:05Z

Contoh

Onnowpurbo at 08:10, 6 March 2020

2020-03-06T08:10:00Z

Onnowpurbo at 03:57, 28 January 2020

2020-01-28T03:57:34Z

Onnowpurbo at 02:47, 23 January 2020

2020-01-23T02:47:26Z

Onnowpurbo at 13:53, 12 January 2020

2020-01-12T13:53:38Z

Onnowpurbo: Created page with " thumb"

2019-07-09T23:40:08Z

Created page with " center|400px|thumb"

New page

[[File:OrangeLogisticRegression.png|center|400px|thumb]]

← Older revision		Revision as of 22:00, 8 April 2020
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	==Youtube==		==Youtube==

		+	* [https://www.youtube.com/watch?v=N8TkXh-8XD4 YOUTUBE: ORANGE penyiapan data untuk penilaian award]
	* [https://www.youtube.com/watch?v=p2YAcpab3gE YOUTUBE: ORANGE AI in awarding process]		* [https://www.youtube.com/watch?v=p2YAcpab3gE YOUTUBE: ORANGE AI in awarding process]

← Older revision		Revision as of 21:59, 8 April 2020
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	[[File:OrangeLogisticRegression.png\|center\|400px\|thumb]]		[[File:OrangeLogisticRegression.png\|center\|400px\|thumb]]
		+
		+	==Youtube==
		+
		+	* [https://www.youtube.com/watch?v=p2YAcpab3gE YOUTUBE: ORANGE AI in awarding process]

	==Referensi==		==Referensi==

@@ Line 29: / Line 29: @@
 Selanjutnya kita ingin memprediksi nilai class di dataset baru. Kita load hayes-roth_test di Widget File ke dua dan menyambungkannya ke Widget Predictions. Kita sekarang dapat mengamati nilai class hasil prediksi Widget Logistic Regression langsung di Widget Predictions.
-[[File:LogisticRegression-classification.png|center|200px|thumb]]
+[[File:LogisticRegression-classification.png|center|600px|thumb]]
 Contoh Workflow lain,

@@ Line 2: / Line 2: @@
-Algoritma klasifikasi logistic regression dengan regularisasi LASSO (L1) atau ridge (L2).
+Widget Logistic Regression mengimplementasikan algoritma klasifikasi logistic regression dengan regularisasi LASSO (L1) atau ridge (L2).
 ==Input==
@@ Line 15: / Line 15: @@
   Coefficients: logistic regression coefficients
-Logistic Regression belajar Logistic Regression model dari data. Dia hanya bisa bekerja / berfungsi untuk task classification.
+Widget Logistic Regression learn (mempelajari) Logistic Regression model dari data. Widget Logistic Regression hanya dapat bekerja / berfungsi untuk task classification.
 [[File:LogisticRegression-stamped.png|center|200px|thumb]]
@@ Line 25: / Line 25: @@
 ==Contoh==
-Widget digunakan sama seperti widget lainnya untuk menginduksi classifier. Ini adalah contoh yang menunjukkan hasil prediksi dengan logistic regression pada dataset hayes-roth. Kita pertama-tama me-load hayes-roth_learn di widget File dan meneruskan data ke Widget Logistic Regression. Lalu kita meneruskan model hasil training ke Widget Predictions.
+Widget Logistic Regression digunakan sama seperti widget lainnya untuk menginduksi classifier. Berikut adalah contoh yang menunjukkan hasil prediksi dengan logistic regression pada dataset hayes-roth. Pertama-tama load hayes-roth_learn dengan widget File dan meneruskan data ke Widget Logistic Regression. Lalu kita meneruskan model hasil training ke Widget Predictions.
-Selanjutnya kita ingin memprediksi nilai class di dataset baru. Kita load hayes-roth_test di Widget File ke dua dan menyambungkannya ke Widget Predictions. Kita sekarag bisa mengamati nilai class hasil prediksi dengan Widget Logistic Regression langsung di Widget Predictions.
+Selanjutnya kita ingin memprediksi nilai class di dataset baru. Kita load hayes-roth_test di Widget File ke dua dan menyambungkannya ke Widget Predictions. Kita sekarang dapat mengamati nilai class hasil prediksi Widget Logistic Regression langsung di Widget Predictions.
 [[File:LogisticRegression-classification.png|center|200px|thumb]]

@@ Line 27: / Line 27: @@
 Widget digunakan sama seperti widget lainnya untuk menginduksi classifier. Ini adalah contoh yang menunjukkan hasil prediksi dengan logistic regression pada dataset hayes-roth. Kita pertama-tama me-load hayes-roth_learn di widget File dan meneruskan data ke Widget Logistic Regression. Lalu kita meneruskan model hasil training ke Widget Predictions.
-Now we want to predict class value on a new dataset. We load hayes-roth_test in the second File widget and connect it to Predictions. We can now observe class values predicted with Logistic Regression directly in Predictions.
+Selanjutnya kita ingin memprediksi nilai class di dataset baru. Kita load hayes-roth_test di Widget File ke dua dan menyambungkannya ke Widget Predictions. Kita sekarag bisa mengamati nilai class hasil prediksi dengan Widget Logistic Regression langsung di Widget Predictions.
 [[File:LogisticRegression-classification.png|center|200px|thumb]]
@@ Line 34: / Line 34: @@
 [[File:OrangeLogisticRegression.png|center|400px|thumb]]
 ==Referensi==

@@ Line 4: / Line 4: @@
 The logistic regression classification algorithm with LASSO (L1) or ridge (L2) regularization.
-Inputs
+==Input==
-    Data: input dataset
+ Data: input dataset
-    Preprocessor: preprocessing method(s)
+ Preprocessor: preprocessing method(s)
-Outputs
+==Output==
-    Learner: logistic regression learning algorithm
+ Learner: logistic regression learning algorithm
-    Model: trained model
+ Model: trained model
-    Coefficients: logistic regression coefficients
+ Coefficients: logistic regression coefficients
 Logistic Regression learns a Logistic Regression model from the data. It only works for classification tasks.
@@ Line 19: / Line 19: @@
 [[File:LogisticRegression-stamped.png|center|200px|thumb]]
-    A name under which the learner appears in other widgets. The default name is “Logistic Regression”.
+* A name under which the learner appears in other widgets. The default name is “Logistic Regression”.
-    Regularization type (either L1 or L2). Set the cost strength (default is C=1).
+* Regularization type (either L1 or L2). Set the cost strength (default is C=1).
-    Press Apply to commit changes. If Apply Automatically is ticked, changes will be communicated automatically.
+* Press Apply to commit changes. If Apply Automatically is ticked, changes will be communicated automatically.
 ==Contoh==
@@ Line 30: / Line 30: @@
 [[File:LogisticRegression-classification.png|center|200px|thumb]]
 Contoh Workflow lain,

← Older revision		Revision as of 13:53, 12 January 2020
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		+	Sumber: https://docs.biolab.si//3/visual-programming/widgets/model/logisticregression.html
		+
		+
		+	The logistic regression classification algorithm with LASSO (L1) or ridge (L2) regularization.
		+
		+	Inputs
		+
		+	Data: input dataset
		+
		+	Preprocessor: preprocessing method(s)
		+
		+	Outputs
		+
		+	Learner: logistic regression learning algorithm
		+
		+	Model: trained model
		+
		+	Coefficients: logistic regression coefficients
		+
		+	Logistic Regression learns a Logistic Regression model from the data. It only works for classification tasks.
		+
		+	../../_images/LogisticRegression-stamped.png
		+
		+	A name under which the learner appears in other widgets. The default name is “Logistic Regression”.
		+
		+	Regularization type (either L1 or L2). Set the cost strength (default is C=1).
		+
		+	Press Apply to commit changes. If Apply Automatically is ticked, changes will be communicated automatically.
		+
		+	Example
		+
		+	The widget is used just as any other widget for inducing a classifier. This is an example demonstrating prediction results with logistic regression on the hayes-roth dataset. We first load hayes-roth_learn in the File widget and pass the data to Logistic Regression. Then we pass the trained model to Predictions.
		+
		+	Now we want to predict class value on a new dataset. We load hayes-roth_test in the second File widget and connect it to Predictions. We can now observe class values predicted with Logistic Regression directly in Predictions.
		+
		+	../../_images/LogisticRegression-classification.png
		+


	[[File:OrangeLogisticRegression.png\|center\|400px\|thumb]]		[[File:OrangeLogisticRegression.png\|center\|400px\|thumb]]
		+
		+
		+	==Referensi==
		+
		+	* https://docs.biolab.si//3/visual-programming/widgets/model/logisticregression.html
		+
		+	==Pranala Menarik==
		+
		+	* [[Orange]]