Posted by , Jan Pfeifer

About decision forests

Decision forests are a family of machine learning algorithms with quality and speed competitive with (and often favorable to) neural networks, especially when you’re working with tabular data. They’re built from many , .

Code example

A good example is worth a thousand words. So in this blog post, we will show how easy it is to train a model with TensorFlow Decision Forests. More examples are available on the . You may also watch .

Let's train a model:

# Install TensorFlow Decision Forests
!pip install tensorflow_decision_forests

# Load TensorFlow Decision Forests
import tensorflow_decision_forests as tfdf

# Load the training dataset using pandas
import pandas
train_df = pandas.read_csv("penguins_train.csv")

# Convert the pandas dataframe into a TensorFlow dataset
train_ds = tfdf.keras.pd_dataframe_to_tf_dataset(train_df, label="species")

# Train the model
model = tfdf.keras.RandomForestModel()
model.fit(train_ds)

Observe that nowhere in the code did we provide input features or hyperparameters. That means, TensorFlow Decision Forests will automatically detect the input features from this dataset and use default values for all hyperparameters.

Evaluating a model

Now, let's evaluate the quality of our model:

# Load the testing dataset
test_df = pandas.read_csv("penguins_test.csv")

# Convert it to a TensorFlow dataset
test_ds = tfdf.keras.pd_dataframe_to_tf_dataset(test_df, label="species")

# Evaluate the model
model.compile(metrics=["accuracy"])
print(model.evaluate(test_ds))
# >> 0.979311
# Note: Cross-validation would be more suited on this small dataset.
# See also the "Out-of-bag evaluation" below.

# Export the model to a TensorFlow SavedModel
model.save("project/my_first_model")

Easy, right? And a default RandomForest model with default hyperparameters provides a quick and good baseline for most problems. Decision forests in general will train quickly for small and medium sized problems, require less hyperparameter tuning compared to many other types of models, and will often provide strong results.

Interpreting a model

Now that you have looked at the accuracy of the trained model, let’s consider its interpretability. Interpretability is important if you wish to understand and explain the phenomenon being modeled, debug a model, or begin to trust its decisions. As noted above, we have provided a number of tools to interpret trained models, beginning with plots.

tfdf.model_plotter.plot_model_in_colab(model, tree_idx=0)