Menambahkan Algoritma pengklasifikasi Hutan Acak #271#272
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slowy07 merged 13 commits intobellshade:mainfrom Jun 6, 2023
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Menambahkan Algoritma pengklasifikasi Hutan Acak #271#272slowy07 merged 13 commits intobellshade:mainfrom
slowy07 merged 13 commits intobellshade:mainfrom
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i think that's problem from linting check (flake8), can you fix that by format and follow the guideline from CONTIRBUTING.md, thanks !
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| """ | ||
| Algoritme hutan acak bekerja dengan membuat ansambel pohon keputusan, di mana setiap pohon dilatih pada subset acak dari data pelatihan. | ||
| Selama prediksi, setiap pohon di hutan secara independen memprediksi label untuk sampel masukan yang diberikan, dan prediksi akhir ditentukan oleh suara terbanyak. | ||
| Pendekatan ansambel ini membantu meningkatkan akurasi dan ketahanan model secara keseluruhan. | ||
| """ |
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| # Kelas DecisionTree mewakili pohon keputusan tunggal di hutan acak. | ||
| # Itu diinisialisasi dengan parameter max_depth (kedalaman maksimum pohon) dan min_samples_leaf (jumlah minimum sampel yang diperlukan untuk membuat simpul daun).# |
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| def fit(self, X, y): | ||
| self.tree = self.build_tree(X, y, depth=0) | ||
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| # Fungsi build_tree secara rekursif membagi data berdasarkan fitur dan nilai terbaik, dengan mempertimbangkan perolehan informasi |
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| entropy = -np.sum(probabilities * np.log2(probabilities + 1e-10)) | ||
| return entropy | ||
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| # Metode prediksi memprediksi label untuk sekumpulan sampel input dengan memanggil fungsi predict_sample untuk setiap sampel. |
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| def prediksi(self, X): | ||
| return np.array([self.predict_sample(x) for x in X]) | ||
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| # Fungsi predict_sample menelusuri pohon keputusan untuk menentukan label prediksi untuk satu sampel masukan. |
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| # Kelas RandomForest mewakili ansambel hutan acak. | ||
| # Itu diinisialisasi dengan parameter num_trees (jumlah pohon di hutan), max_depth (kedalaman maksimum setiap pohon), dan min_samples_leaf (jumlah minimum sampel yang diperlukan untuk membuat simpul daun). |
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| self.min_samples_leaf = min_samples_leaf | ||
| self.trees = [] | ||
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| # Metode fit melatih hutan acak dengan membuat pohon keputusan num_trees dan menyesuaikannya pada himpunan bagian acak dari data. |
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| tree.fit(X[indices], y[indices]) | ||
| self.trees.append(tree) | ||
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| # Metode prediksi membuat prediksi untuk satu set sampel input dengan menggabungkan prediksi dari setiap pohon dan mengambil suara mayoritas. |
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Thanks for the guidance.
…itma random forest classifier. bellshade#272
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Please review it. |
slowy07
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LGTM, thanks for contribution @ShubhamZoro !
linked issue #246
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LGTM I will look into this issue. |
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Deskripsi (Description)
Checklist:
Umum:
Contributor Requirements (Syarat Kontributor) dan Lain-Lain:
Unit Testing dan Linting:
Environment
Saya menggunakan (I'm using):
os=linux / windows / macOSpython=python3 -V (unix) / py -3 -V (Windows-Python launcher)linked issue #NOMOR_ISSUE