Orange: Linear Regression - Revision history

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2020-04-06T02:11:12Z

Output

Onnowpurbo at 10:23, 5 April 2020

2020-04-05T10:23:24Z

Onnowpurbo: /* Contoh */

2020-04-05T10:15:16Z

Contoh

Onnowpurbo at 07:53, 6 March 2020

2020-03-06T07:53:03Z

Onnowpurbo at 03:56, 28 January 2020

2020-01-28T03:56:39Z

Onnowpurbo at 02:44, 23 January 2020

2020-01-23T02:44:31Z

Onnowpurbo: Created page with "Sumber: https://docs.biolab.si//3/visual-programming/widgets/model/linearregression.html A linear regression algorithm with optional L1 (LASSO), L2 (ridge) or L1L2 (elastic..."

2020-01-12T13:49:56Z

Created page with "Sumber: https://docs.biolab.si//3/visual-programming/widgets/model/linearregression.html A linear regression algorithm with optional L1 (LASSO), L2 (ridge) or L1L2 (elastic..."

New page

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

A linear regression algorithm with optional L1 (LASSO), L2 (ridge) or L1L2 (elastic net) regularization.

Inputs

Data: input dataset

Preprocessor: preprocessing method(s)

Outputs

Learner: linear regression learning algorithm

Model: trained model

Coefficients: linear regression coefficients

The Linear Regression widget constructs a learner/predictor that learns a linear function from its input data. The model can identify the relationship between a predictor xi and the response variable y. Additionally, Lasso and Ridge regularization parameters can be specified. Lasso regression minimizes a penalized version of the least squares loss function with L1-norm penalty and Ridge regularization with L2-norm penalty.

Linear regression works only on regression tasks.

../../_images/LinearRegression-stamped.png

The learner/predictor name

Choose a model to train:

no regularization

a Ridge regularization (L2-norm penalty)

a Lasso bound (L1-norm penalty)

an Elastic net regularization

Produce a report.

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

Example

Below, is a simple workflow with housing dataset. We trained Linear Regression and Random Forest and evaluated their performance in Test & Score.

../../_images/LinearRegression-regression.png

==Referensi==

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

==Pranala Menarik==

* [[Orange]]

@@ Line 17: / Line 17: @@
 Widget Linear Regression membangun learner/predictor yang akan learn fungsi linear function dari input data-nya. Model dapat mengidentifikasi hubungan antara predictor xi dan response variable y. Di samping itu, parameter regularisasi Lasso and Ridge dapat di spesifikasikan. Lasso regression meminimalisasi penalized version dari least squares loss function dengan L1-norm penalty sedangkan regularisai Ridge dengan L2-norm penalty.
-Widget Linear regression hanya dapat berfungsi / bekerja pada task regression.
+Widget Linear Regression hanya dapat berfungsi / bekerja pada regression task.
 [[File:LinearRegression-stamped.png|center|200px|thumb]]

@@ Line 2: / Line 2: @@
-Sebuah algoritma linear regression dengan regularisasi optional L1 (LASSO), L2 (ridge) atau L1L2 (elastic net).
+Widget Linear Regression mengimplementasikan sebuah algoritma linear regression dengan regularisasi optional L1 (LASSO), L2 (ridge) atau L1L2 (elastic net).
 ==Input==
@@ Line 17: / Line 17: @@
 Widget Linear Regression membangun learner/predictor yang akan learn fungsi linear function dari input data-nya. Model dapat mengidentifikasi hubungan antara predictor xi dan response variable y. Di samping itu, parameter regularisasi Lasso and Ridge dapat di spesifikasikan. Lasso regression meminimalisasi penalized version dari least squares loss function dengan L1-norm penalty sedangkan regularisai Ridge dengan L2-norm penalty.
-Linear regression hanya dapat berfungsi / bekerja pada task regression.
+Widget Linear regression hanya dapat berfungsi / bekerja pada task regression.
 [[File:LinearRegression-stamped.png|center|200px|thumb]]

@@ Line 33: / Line 33: @@
 ==Contoh==
-Di bawah ini adalah workflow sederhana dengan housing dataset. Kita men-train Linear Regression dan Random Forest dan mengevaluasi performance-nya di Test & Score.
+Di bawah ini adalah workflow sederhana dengan housing dataset. Kita men-train widget Linear Regression dan widget Random Forest dan mengevaluasi performance-nya di widget Test & Score.
 ==Referensi==

@@ Line 2: / Line 2: @@
-A linear regression algorithm with optional L1 (LASSO), L2 (ridge) or L1L2 (elastic net) regularization.
+Sebuah algoritma linear regression dengan regularisasi optional L1 (LASSO), L2 (ridge) atau L1L2 (elastic net).
 ==Input==
@@ Line 15: / Line 15: @@
   Coefficients: linear regression coefficients
-The Linear Regression widget constructs a learner/predictor that learns a linear function from its input data. The model can identify the relationship between a predictor xi and the response variable y. Additionally, Lasso and Ridge regularization parameters can be specified. Lasso regression minimizes a penalized version of the least squares loss function with L1-norm penalty and Ridge regularization with L2-norm penalty.
+Widget Linear Regression membangun learner/predictor yang akan learn fungsi linear function dari input data-nya. Model dapat mengidentifikasi hubungan antara predictor xi dan response variable y. Di samping itu, parameter regularisasi Lasso and Ridge dapat di spesifikasikan. Lasso regression meminimalisasi penalized version dari least squares loss function dengan L1-norm penalty sedangkan regularisai Ridge dengan L2-norm penalty.
-Linear regression works only on regression tasks.
+Linear regression hanya dapat berfungsi / bekerja pada task regression.
 [[File:LinearRegression-stamped.png|center|200px|thumb]]
@@ Line 33: / Line 33: @@
 ==Contoh==
-Below, is a simple workflow with housing dataset. We trained Linear Regression and Random Forest and evaluated their performance in Test & Score.
+Di bawah ini adalah workflow sederhana dengan housing dataset. Kita men-train Linear Regression dan Random Forest dan mengevaluasi performance-nya di Test & Score.
 [[File:LinearRegression-regression.png|center|200px|thumb]]

@@ Line 4: / Line 4: @@
 A linear regression algorithm with optional L1 (LASSO), L2 (ridge) or L1L2 (elastic net) regularization.
-Inputs
+==Input==
-    Data: input dataset
+ Data: input dataset
-    Preprocessor: preprocessing method(s)
+ Preprocessor: preprocessing method(s)
-Outputs
+==Output==
-    Learner: linear regression learning algorithm
+ Learner: linear regression learning algorithm
-    Model: trained model
+ Model: trained model
-    Coefficients: linear regression coefficients
+ Coefficients: linear regression coefficients
 The Linear Regression widget constructs a learner/predictor that learns a linear function from its input data. The model can identify the relationship between a predictor xi and the response variable y. Additionally, Lasso and Ridge regularization parameters can be specified. Lasso regression minimizes a penalized version of the least squares loss function with L1-norm penalty and Ridge regularization with L2-norm penalty.
@@ Line 21: / Line 21: @@
 [[File:LinearRegression-stamped.png|center|200px|thumb]]
-    The learner/predictor name
+* The learner/predictor name
-    Choose a model to train:
+* Choose a model to train:
-        no regularization
+** no regularization
-        a Ridge regularization (L2-norm penalty)
+** a Ridge regularization (L2-norm penalty)
-        a Lasso bound (L1-norm penalty)
+** a Lasso bound (L1-norm penalty)
-        an Elastic net regularization
+** an Elastic net regularization
-    Produce a report.
-    Press Apply to commit changes. If Apply Automatically is ticked, changes are committed automatically.
+* Produce a report.
 ==Contoh==