# Install TF-DF !pip install tensorflow tensorflow_decision_forests # Load TF-DF import tensorflow_decision_forests as tfdf import pandas as pd # Load a dataset in a Pandas dataframe. train_df = pd.read_csv("project/train.csv") test_df = pd.read_csv("project/test.csv") # Convert the dataset into a TensorFlow dataset. train_ds = tfdf.keras.pd_dataframe_to_tf_dataset(train_df, label="my_label") test_ds = tfdf.keras.pd_dataframe_to_tf_dataset(test_df, label="my_label") # Train a Random Forest model. model = tfdf.keras.RandomForestModel() model.fit(train_ds) # Summary of the model structure. model.summary() # Compute model accuracy. model.compile(metrics=["accuracy"]) model.evaluate(test_ds, return_dict=True) # Export the model to a SavedModel. model.save("project/model")
# Install YDF !pip install ydf -U import ydf import pandas as pd # Load a 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") # Export the model as a TensorFlow Saved Model model.to_tensorflow_saved_model("/tmp/my_saved_model")
![](https://www.tensorflow.org/static/decision_forests/image/ydf_logo.png?authuser=7&hl=id)
YDF adalah perpustakaan baru Google untuk melatih Decision Forests.
YDF memperluas kekuatan TF-DF, menawarkan fitur-fitur baru, API yang disederhanakan, waktu pelatihan yang lebih cepat, dokumentasi yang diperbarui, dan peningkatan kompatibilitas dengan perpustakaan ML populer.
TensorFlow Decision Forests ( TF-DF ) adalah perpustakaan untuk melatih, menjalankan, dan menafsirkan model hutan keputusan (misalnya, Random Forests, Gradient Boosted Trees) di TensorFlow. TF-DF mendukung klasifikasi, regresi, pemeringkatan, dan peningkatan.
Kata Kunci: Decision Forests, TensorFlow, Random Forest, Gradient Boosted Trees, CART, interpretasi model.Dokumentasi & Sumber Daya
Sumber daya berikut tersedia:
- Panduan dan tutorial
- Referensi API
- Dokumentasi YDF (juga berlaku untuk TF-DF)
- Kelas online Hutan Keputusan Pengembang Google