Difference between revisions of "Orange: Logistic Regression"

Latest revision as of 05:00, 9 April 2020

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

Widget Logistic Regression mengimplementasikan algoritma klasifikasi logistic regression dengan regularisasi LASSO (L1) atau ridge (L2).

Input

Data: input dataset
Preprocessor: preprocessing method(s)

Output

Learner: logistic regression learning algorithm
Model: trained model
Coefficients: logistic regression coefficients

Widget Logistic Regression learn (mempelajari) Logistic Regression model dari data. Widget Logistic Regression hanya dapat bekerja / berfungsi untuk task classification.

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.

Contoh

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 sekarang dapat mengamati nilai class hasil prediksi Widget Logistic Regression langsung di Widget Predictions.

Contoh Workflow lain,

Youtube

Referensi

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

Pranala Menarik

Orange

@@ Line 2: / Line 2: @@
-The logistic regression classification algorithm with LASSO (L1) or ridge (L2) regularization.
+Widget Logistic Regression mengimplementasikan algoritma klasifikasi logistic regression dengan regularisasi LASSO (L1) atau ridge (L2).
-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.
+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]]
-    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==
-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.
+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.
-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 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]]
-../../_images/LogisticRegression-classification.png
 Contoh Workflow lain,
@@ Line 36: / Line 35: @@
 [[File:OrangeLogisticRegression.png|center|400px|thumb]]
+==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]
 ==Referensi==