Orange: Calibrated Learner - Revision history

Onnowpurbo: /* Youtube */

2020-04-10T22:55:38Z

Youtube

Onnowpurbo: /* Referensi */

2020-04-10T22:54:28Z

Referensi

Onnowpurbo: /* Contoh */

2020-04-10T22:21:13Z

Contoh

Onnowpurbo: /* Output */

2020-03-03T04:10:10Z

Output

Onnowpurbo: /* Contoh */

2020-03-02T10:40:54Z

Contoh

Onnowpurbo at 10:37, 2 March 2020

2020-03-02T10:37:13Z

Onnowpurbo at 03:31, 28 January 2020

2020-01-28T03:31:49Z

Onnowpurbo at 01:48, 23 January 2020

2020-01-23T01:48:55Z

Onnowpurbo: Created page with "Sumber: https://docs.biolab.si//3/visual-programming/widgets/model/calibratedlearner.html Wraps another learner with probability calibration and decision threshold optimizati..."

2020-01-12T13:31:03Z

Created page with "Sumber: https://docs.biolab.si//3/visual-programming/widgets/model/calibratedlearner.html Wraps another learner with probability calibration and decision threshold optimizati..."

New page

Sumber: https://docs.biolab.si//3/visual-programming/widgets/model/calibratedlearner.html

Wraps another learner with probability calibration and decision threshold optimization.

Inputs

Data: input dataset

Preprocessor: preprocessing method(s)

Base Learner: learner to calibrate

Outputs

Learner: calibrated learning algorithm

Model: trained model using the calibrated learner

This learner produces a model that calibrates the distribution of class probabilities and optimizes decision threshold. The widget works only for binary classification tasks.

../../_images/Calibrated-Learner-stamped.png

The name under which it will appear in other widgets. Default name is composed of the learner, calibration and optimization parameters.

Probability calibration:

Sigmoid calibration

Isotonic calibration

No calibration

Decision threshold optimization:

Optimize classification accuracy

Optimize F1 score

No threshold optimization

Press Apply to commit changes. If Apply Automatically is ticked, changes are committed automatically.

Example

A simple example with Calibrated Learner. We are using the titanic data set as the widget requires binary class values (in this case they are ‘survived’ and ‘not survived’).

We will use Logistic Regression as the base learner which will we calibrate with the default settings, that is with sigmoid optimization of distribution values and by optimizing the CA.

Comparing the results with the uncalibrated Logistic Regression model we see that the calibrated model performs better.

../../_images/Calibrated-Learner-Example.png

==Referensi==

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

==Pranala Menarik==

* [[Orange]]

← Older revision		Revision as of 22:55, 10 April 2020
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	* [https://www.youtube.com/watch?v=fYLD2AHjjZI YOUTUBE: ORANGE model Callibrated Learner]		* [https://www.youtube.com/watch?v=fYLD2AHjjZI YOUTUBE: ORANGE model Callibrated Learner]
−

	==Referensi==		==Referensi==

← Older revision		Revision as of 22:54, 10 April 2020
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	[[File:Calibrated-Learner-Example.png\|center\|600px\|thumb]]		[[File:Calibrated-Learner-Example.png\|center\|600px\|thumb]]
		+
		+	==Youtube==
		+
		+	* [https://www.youtube.com/watch?v=fYLD2AHjjZI YOUTUBE: ORANGE model Callibrated Learner]
		+

	==Referensi==		==Referensi==

@@ Line 40: / Line 40: @@
 Membandingkan hasil dari uncalibrated Logistic Regression model kita akan melihat dengan jelas bahwa calibrated model lebih baik.
-[[File:Calibrated-Learner-Example.png|center|200px|thumb]]
+[[File:Calibrated-Learner-Example.png|center|600px|thumb]]
 ==Referensi==

@@ Line 14: / Line 14: @@
   Model: trained model using the calibrated learner
-Learner ini menghasilkan sebuah model yang mengkalibrasi distribusi dari class probabilities dan meng-optimizes decision threshold. Widget ini hanya bekerja untuk binary classification task saja.
+Widget Calibrated Learner menghasilkan sebuah model yang mengkalibrasi distribusi dari class probabilities dan meng-optimasi decision threshold. Widget ini hanya bekerja untuk binary classification task saja.
 [[File:Calibrated-Learner-stamped.png|center|200px|thumb]]

@@ Line 34: / Line 34: @@
 ==Contoh==
-A simple example with Calibrated Learner. We are using the titanic data set as the widget requires binary class values (in this case they are ‘survived’ and ‘not survived’).
+Contoh sederhana dengan Calibrated Learner. Kita menggunakan dataset titanic karena widget ini membutuhkan nilai binary class (dalam hal ini mereka adalah 'survived’ atau ‘not survived’).
-We will use Logistic Regression as the base learner which will we calibrate with the default settings, that is with sigmoid optimization of distribution values and by optimizing the CA.
+Kita menggunakan Logistic Regression sebagai base learner yang akan dikalibrasi dengan nilai setting default, yaitu dengan sigmoid optimization dari distribusi nilai dan di optimasi dengan CA.
-Comparing the results with the uncalibrated Logistic Regression model we see that the calibrated model performs better.
+Membandingkan hasil dari uncalibrated Logistic Regression model kita akan melihat dengan jelas bahwa calibrated model lebih baik.
 [[File:Calibrated-Learner-Example.png|center|200px|thumb]]
 ==Referensi==

@@ Line 1: / Line 1: @@
 Sumber: https://docs.biolab.si//3/visual-programming/widgets/model/calibratedlearner.html
-Wraps another learner with probability calibration and decision threshold optimization.
+Membungkus / melanjutkan kerja dari learner lain dengan probability calibration dan decision threshold optimization.
 ==Input==
@@ Line 14: / Line 14: @@
   Model: trained model using the calibrated learner
-This learner produces a model that calibrates the distribution of class probabilities and optimizes decision threshold. The widget works only for binary classification tasks.
+Learner ini menghasilkan sebuah model yang mengkalibrasi distribusi dari class probabilities dan meng-optimizes decision threshold. Widget ini hanya bekerja untuk binary classification task saja.
 [[File:Calibrated-Learner-stamped.png|center|200px|thumb]]

@@ Line 3: / Line 3: @@
 Wraps another learner with probability calibration and decision threshold optimization.
-Inputs
+==Input==
-    Data: input dataset
+ Data: input dataset
-    Preprocessor: preprocessing method(s)
+ Preprocessor: preprocessing method(s)
-    Base Learner: learner to calibrate
+ Base Learner: learner to calibrate
-Outputs
+==Output==
-    Learner: calibrated learning algorithm
+ Learner: calibrated learning algorithm
-    Model: trained model using the calibrated learner
+ Model: trained model using the calibrated learner
 This learner produces a model that calibrates the distribution of class probabilities and optimizes decision threshold. The widget works only for binary classification tasks.
@@ Line 19: / Line 19: @@
-    The name under which it will appear in other widgets. Default name is composed of the learner, calibration and optimization parameters.
+* The name under which it will appear in other widgets. Default name is composed of the learner, calibration and optimization parameters.
-    Probability calibration:
+* Decision threshold optimization:
-        Sigmoid calibration
+** Optimize classification accuracy
-        Isotonic calibration
+** Optimize F1 score
-        No calibration
+** No threshold optimization
-    Decision threshold optimization:
+* Press Apply to commit changes. If Apply Automatically is ticked, changes are committed automatically.
 ==Contoh==