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yggdrasil decision forests

C/C++

Python API 0.9.0

Unduh

YDF (Yggdrasil Decision Forests) adalah perpustakaan untuk melatih, mengevaluasi, menafsirkan, dan melayani hutan acak, pohon keputusan yang ditingkatkan gradien, gerobak dan model hutan isolasi.

Lihat dokumentasi untuk informasi lebih lanjut tentang YDF.

Instalasi

Untuk menginstal YDF dari PYPI, jalankan:

pip install ydf -U

Contoh Penggunaan

 import ydf
import pandas as pd

# Load dataset with Pandas
ds_path = "https://raw.githubusercontent.com/google/yggdrasil-decision-forests/main/yggdrasil_decision_forests/test_data/dataset/"
train_ds = pd . read_csv ( ds_path + "adult_train.csv" )
test_ds = pd . read_csv ( ds_path + "adult_test.csv" )

# Train a Gradient Boosted Trees model
model = ydf . GradientBoostedTreesLearner ( label = "income" ). train ( train_ds )

# Look at a model (input features, training logs, structure, etc.)
model . describe ()

# Evaluate a model (e.g. roc, accuracy, confusion matrix, confidence intervals)
model . evaluate ( test_ds )

# Generate predictions
model . predict ( test_ds )

# Analyse a model (e.g. partial dependence plot, variable importance)
model . analyze ( test_ds )

# Benchmark the inference speed of a model
model . benchmark ( test_ds )

# Save the model
model . save ( "/tmp/my_model" )

Contoh dengan API C ++.

 auto dataset_path = " csv:train.csv " ;

// List columns in training dataset
DataSpecification spec;
CreateDataSpec (dataset_path, false , {}, &spec);

// Create a training configuration
TrainingConfig train_config;
train_config.set_learner( " RANDOM_FOREST " );
train_config.set_task(Task::CLASSIFICATION);
train_config.set_label( " my_label " );

// Train model
std::unique_ptr<AbstractLearner> learner;
GetLearner (train_config, &learner);
auto model = learner-> Train (dataset_path, spec);

// Export model
SaveModel ( " my_model " , model.get());

(Berdasarkan contoh/pemula.cc)

Langkah selanjutnya

Periksa tutorial memulai ?.

Kutipan

Jika Anda menggunakan hutan keputusan Yggdrasil dalam publikasi ilmiah, silakan mengutip makalah berikut: Yggdrasil Decision Forests: Perpustakaan Hutan Keputusan yang Cepat dan Dapat Diperpanjang.

Bibtex

 @inproceedings{GBBSP23,
  author       = {Mathieu Guillame{-}Bert and
                  Sebastian Bruch and
                  Richard Stotz and
                  Jan Pfeifer},
  title        = {Yggdrasil Decision Forests: {A} Fast and Extensible Decision Forests
                  Library},
  booktitle    = {Proceedings of the 29th {ACM} {SIGKDD} Conference on Knowledge Discovery
                  and Data Mining, {KDD} 2023, Long Beach, CA, USA, August 6-10, 2023},
  pages        = {4068--4077},
  year         = {2023},
  url          = {https://doi.org/10.1145/3580305.3599933},
  doi          = {10.1145/3580305.3599933},
}

Mentah

Yggdrasilil Decision Forests: Perpustakaan Hutan Pengambilan Keputusan yang Cepat dan Diperpanjang, Guillame-Bert et al., KDD 2023: 4068-4077. doi: 10.1145/3580305.3599933