5 Innovative Ways to Use TensorFlow with Boosted Trees in Python

Rate this post

πŸ’‘ Problem Formulation: Gradient boosting is a powerful machine learning technique that creates an ensemble of decision trees to improve prediction accuracy. This article discusses how TensorFlow, an end-to-end open-source platform for machine learning, can be integrated with boosted trees to implement models in Python. This integration allows for leveraging TensorFlow’s scalability and boosted trees’ effectiveness. Readers will learn how these methods can predict binary outcomes, like classifying whether an email is spam (input) and the outcome is a binary indicator (output).

Method 1: Use TensorFlow’s Estimator API for Boosted Trees

This method involves utilizing TensorFlow’s built-in Estimator API, specifically the tf.estimator.BoostedTreesClassifier. This API simplifies the machine learning workflow, handling training, evaluation, prediction, and export for serving. It’s highly scalable and integrates seamlessly with TensorFlow’s ecosystem, enabling boosted trees algorithms to take advantage of TensorFlow’s features like distributed training and automatic differentiation.

Here’s an example:

import tensorflow as tf
from sklearn.datasets import load_breast_cancer

# Load dataset.
data, target = load_breast_cancer(return_X_y=True)

# Create input functions.
input_fn = tf.compat.v1.estimator.inputs.numpy_input_fn(
    x={"x": data},

# Create BoostedTreesClassifier.
feature_columns = [tf.feature_column.numeric_column("x", shape=[data.shape[1]])]
bt_classifier = tf.estimator.BoostedTreesClassifier(feature_columns, n_batches_per_layer=1)

# Train the model.
bt_classifier.train(input_fn=input_fn, steps=100)


The model is trained for 100 steps and is ready to make predictions or be evaluated.

This code snippet loads the breast cancer dataset and prepares an input function for the TensorFlow Estimator. It then defines numerical feature columns, initiates the BoostedTreesClassifier, and finally trains the model for 100 steps using the provided input function.

Method 2: Custom Training Loop with GradientTape

Another approach is implementing a custom training loop using TensorFlow’s tf.GradientTape, which is particularly useful when you need more control and customization over the model training process. GradientTape records operations for automatic differentiation, allowing the integration of boosted trees with the rest of TensorFlow’s operations. This method is also favorable for researchers attempting to develop novel boosting algorithms.

Here’s an example:

import tensorflow as tf

# Custom training loop.
def train_step(data, target, model, optimizer, loss_fn):
    with tf.GradientTape() as tape:
        predictions = model(data)
        loss = loss_fn(target, predictions)
    gradients = tape.gradient(loss, model.trainable_variables)
    optimizer.apply_gradients(zip(gradients, model.trainable_variables))

# Assume `model`, `optimizer`, and `loss_fn` are predefined.
# This function would be called within a loop over the dataset.


Applies the gradients to the model’s trainable variables after each training step.

This example defines a custom training step that can be included in a training loop. Inside the train_step function, it records the operations using tf.GradientTape, calculates the loss, computes the gradients, and applies them to the model’s variables. This technique is ideal for those needing specific training requirements unsatisfied by pre-built estimators.

Method 3: Integration with TensorFlow Datasets (TFDS)

TensorFlow Datasets (TFDS) can be used to provide a high-level abstraction for managing data loading and preprocessing. Combining TFDS with boosted trees allows for the use of a vast repository of prepared datasets in a way that is highly compatible with TensorFlow’s model-building APIs. This method streamlines the process from data acquisition to model evaluation.

Here’s an example:

import tensorflow as tf
import tensorflow_datasets as tfds
from sklearn.ensemble import GradientBoostingClassifier

# Load a dataset.
ds_train, ds_info = tfds.load('titanic', split='train', with_info=True)

# Prepare the dataset.
def preprocess(features):
    # Preprocess the features.
    return features

ds_train = ds_train.map(preprocess)
# Further dataset preparation code...

# Combine TFDS with sklearn’s GradientBoostingClassifier.
# Note: This is a simplified representation.
gb_clf = GradientBoostingClassifier(n_estimators=100)
# Assume `X_train` and `y_train` are prepared from `ds_train`.
gb_clf.fit(X_train, y_train)


The GradientBoostingClassifier is trained on the processed dataset.

This code utilizes TFDS to load the Titanic dataset and preprocess it before training with sklearn’s GradientBoostingClassifier. While this example uses an sklearn model, the principle is the same when combining TensorFlow’s boosted tree estimators with TFDS.

Method 4: Using BoostedTrees with Keras Functional API

TensorFlow’s Keras Function API allows for the construction of complex models that may include boosted trees as part of a larger architecture. It offers flexibility in model design and can be quite powerful when combined with TensorFlow’s other functionalities. This method is recommended for those who wish to integrate boosted trees within a deep learning framework or as part of a multi-stage learning pipeline.

Here’s an example:

# Note: There are currently no direct implementations of boosted trees within Keras API.
# This example is hypothetical and for illustrative purposes.

import tensorflow as tf

def build_boosted_trees_model():
    inputs = tf.keras.Input(shape=(data.shape[1],))
    x = tf.keras.layers.Dense(100, activation='relu')(inputs)
    outputs = BoostedTreesModule(x) # Hypothetical boosted trees layer
    model = tf.keras.Model(inputs=inputs, outputs=outputs)
    return model

# Model training and deployment would follow.


Hypothetical Keras model utilizing a boosted trees layer is built and ready for training.

This snippet suggests how one might construct a functional model with a hypothetical boosted trees layer in Keras. It starts with input definition, piping the input through a dense layer before feeding it into a boosted trees module. Although there’s no native support for a boosted trees layer in Keras at this time, the concept illustrates the potential for hybrid modeling within TensorFlow’s expansive API.

Bonus One-Liner Method 5: Quick Model Creation with tf.estimator.BoostedTreesClassifier

For quickly creating a boosted trees model with minimal code, TensorFlow’s Estimator API provides a concise, one-liner setup, assuming feature columns and the input function have been defined.

Here’s an example:

# Assuming the feature_columns and input_fn are already defined.
bt_classifier = tf.estimator.BoostedTreesClassifier(feature_columns).train(input_fn, steps=100)


A boosted trees model is instantiated and trained in just one line of code.

This one-liner demonstrates TensorFlow’s ability to condense the model instantiation and training process into a single line, provided that the necessary components have been established beforehand. It’s an efficient way to quickly prototype boosted trees models in Python.


  • Method 1: Use TensorFlow’s Estimator API for Boosted Trees. This offers a high-level, easily scalable way to implement boosted trees models. However, it lacks the finer control required for certain custom applications.
  • Method 2: Custom Training Loop with GradientTape. This approach provides granular control over the training process, beneficial for research and custom algorithm development. It can, however, be complex and less straightforward than using high-level APIs.
  • Method 3: Integration with TensorFlow Datasets (TFDS). This method eases data handling and preprocessing, seamlessly connecting with TensorFlow’s workflow. There may be limitations in dataset customization that require additional preprocessing steps.
  • Method 4: Using BoostedTrees with Keras Functional API. It’s promising for integrating boosted trees into sophisticated architectures, though such functionality is not yet natively supported within Keras and requires custom implementation.
  • Method 5: Quick Model Creation with tf.estimator.BoostedTreesClassifier. Ideal for rapid prototyping with TensorFlow’s high-level API, but it might not suit complex or specific model configurations.