Difference between revisions of "Orange: Logistic Regression"

Revision as of 15:10, 6 March 2020

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

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

Logistic Regression belajar Logistic Regression model dari data. Dia hanya bisa 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 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.

Contoh Workflow lain,

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.
+Algoritma klasifikasi logistic regression dengan regularisasi LASSO (L1) atau ridge (L2).
 ==Input==
@@ Line 15: / Line 15: @@
   Coefficients: logistic regression coefficients
-Logistic Regression learns a Logistic Regression model from the data. It only works for classification tasks.
+Logistic Regression belajar Logistic Regression model dari data. Dia hanya bisa bekerja / berfungsi untuk task classification.
 [[File:LogisticRegression-stamped.png|center|200px|thumb]]
@@ Line 25: / Line 25: @@
 ==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 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.