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BinBoost

Gradient Boosting Berbasis Aturan Logika Adaptif untuk Fitur Biner

BinBoost adalah algoritma klasifikasi gradient boosting yang membangun ensemble aturan logika murni (AND, OR, XOR) dengan binarisasi fitur numerik adaptif berbasis gradien pada setiap iterasi boosting. Setiap weak learner berupa aturan yang dapat dibaca langsung oleh manusia tanpa memerlukan alat bantu penjelasan pasca-pelatihan.

Kebaruan Utama

  • Binarisasi adaptif berbasis gradien: nilai ambang batas fitur numerik dicari per iterasi untuk memaksimalkan korelasi dengan gradien saat ini
  • Ensemble aturan logika murni: tidak ada pohon keputusan, setiap weak learner adalah aturan seperti (A AND B) atau (C OR D)

Instalasi

pip install binboost

Penggunaan Dasar

import numpy as np
from binboost import BinBoost

X = np.array([[1, 0, 1], [0, 1, 0], [1, 1, 0], [0, 0, 1]], dtype=float)
y = np.array([1, 0, 1, 0])

model = BinBoost(n_estimators=50, learning_rate=0.1, max_rule_length=2)
model.fit(X, y)

print(model.predict(X))
print(model.predict_proba(X))
print(model.rules_)

import pandas as pd
print(pd.DataFrame(model.rule_summary_))

Hyperparameter Utama

Parameter Bawaan Keterangan
n_estimators 100 Jumlah iterasi boosting
learning_rate 0.1 Faktor penyusutan tiap aturan
loss 'logistic' Fungsi loss: 'logistic', 'focal', 'poly'
max_rule_length 2 Jumlah maksimum fitur dalam satu aturan
operators ['AND','OR'] Operator logika yang digunakan
beam_width 5 Lebar beam search
binarize_strategy 'gradient' Strategi binarisasi: 'gradient', 'quantile', 'uniform', 'kmeans'
subsample 0.8 Fraksi data per iterasi
feature_selection_threshold 0.01 Ambang batas seleksi fitur berbasis gradien

Fitur yang Didukung

  • Fitur biner (0/1): langsung diproses
  • Fitur numerik (int/float): dibinarisasi otomatis
  • Fitur kategorikal 3+ kelas: wajib OneHotEncode terlebih dahulu
from sklearn.preprocessing import OneHotEncoder
enc = OneHotEncoder(sparse_output=False, drop='first')
X_encoded = enc.fit_transform(X[['kolom_kategorikal']])

BinBoost secara otomatis mendeteksi kelompok fitur OneHotEncoding dan mencegah aturan yang tidak masuk akal seperti (Warna_Merah AND Warna_Biru).

Atribut Setelah Pelatihan

model.rules_               # daftar teks aturan
model.rule_weights_        # bobot setiap aturan
model.feature_importances_ # skor kepentingan fitur
model.train_score_         # loss per iterasi
model.rule_summary_        # ringkasan lengkap atau konversi ke DataFrame
model.n_rules_             # jumlah aturan aktif
model.feature_usage_       # frekuensi penggunaan tiap fitur

Lisensi

MIT

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