Orange: Logistic Regression

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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.