python-tensorflowHow can I use Python and TensorFlow to build a decision tree?

In order to use Python and TensorFlow to build a decision tree, you need to first install TensorFlow on your machine. After that, you can use the tf.contrib.learn.DNNClassifier class from TensorFlow to build a decision tree.

Example code

# Import TensorFlow
import tensorflow as tf

# Create feature columns
feature_columns = [tf.contrib.layers.real_valued_column("x", dimension=4)]

# Create a deep neural network classifier
classifier = tf.contrib.learn.DNNClassifier(feature_columns=feature_columns,
                                            hidden_units=[10, 20, 10],
                                            n_classes=3,
                                            model_dir="/tmp/dnn_model")

# Train the model
classifier.fit(x=X_train, y=y_train, steps=200)

# Evaluate the model
accuracy_score = classifier.evaluate(x=X_test, y=y_test)["accuracy"]
print('Accuracy: {0:f}'.format(accuracy_score))

The code above will create a deep neural network classifier with three hidden layers of 10, 20 and 10 nodes respectively. The X_train and y_train parameters will be used to train the model and the X_test and y_test parameters will be used to evaluate the model. The accuracy_score variable will contain the accuracy of the model.

Helpful links

• TensorFlow documentation
• Tutorial on Building Decision Trees with TensorFlow

Edit this code on GitHub