Two-Class Decision Forest component

This article describes a component in Azure Machine Learning designer.

Use this component to create a machine learning model based on the decision forests algorithm.

Decision forests are fast, supervised ensemble models. This component is a good choice if you want to predict a target with a maximum of two outcomes.

Understanding decision forests

This decision forest algorithm is an ensemble learning method intended for classification tasks. Ensemble methods are based on the general principle that rather than relying on a single model, you can get better results and a more generalized model by creating multiple related models and combining them in some way. Generally, ensemble models provide better coverage and accuracy than single decision trees.

There are many ways to create individual models and combine them in an ensemble. This particular implementation of a decision forest works by building multiple decision trees and then voting on the most popular output class. Voting is one of the better-known methods for generating results in an ensemble model.

  • Many individual classification trees are created, using the entire dataset, but different (usually randomized) starting points. This differs from the random forest approach, in which the individual decision trees might only use some randomized portion of the data or features.
  • Each tree in the decision forest tree outputs a non-normalized frequency histogram of labels.
  • The aggregation process sums these histograms and normalizes the result to get the "probabilities" for each label.
  • The trees that have high prediction confidence will have a greater weight in the final decision of the ensemble.

Decision trees in general have many advantages for classification tasks:

  • They can capture non-linear decision boundaries.
  • You can train and predict on lots of data, as they are efficient in computation and memory usage.
  • Feature selection is integrated in the training and classification processes.
  • Trees can accommodate noisy data and many features.
  • They are non-parametric models, meaning they can handle data with varied distributions.

However, simple decision trees can overfit on data, and are less generalizable than tree ensembles.

For more information, see Decision Forests.

How to configure

  1. Add the Two-Class Decision Forest component to your pipeline in Azure Machine Learning, and open the Properties pane of the component.

    You can find the component under Machine Learning. Expand Initialize, and then Classification.

  2. For Resampling method, choose the method used to create the individual trees. You can choose from Bagging or Replicate.

    • Bagging: Bagging is also called bootstrap aggregating. In this method, each tree is grown on a new sample, created by randomly sampling the original dataset with replacement until you have a dataset the size of the original.

      The outputs of the models are combined by voting, which is a form of aggregation. Each tree in a classification decision forest outputs an unnormalized frequency histogram of labels. The aggregation is to sum these histograms and normalize to get the "probabilities" for each label. In this manner, the trees that have high prediction confidence will have a greater weight in the final decision of the ensemble.

      For more information, see the Wikipedia entry for Bootstrap aggregating.

    • Replicate: In replication, each tree is trained on exactly the same input data. The determination of which split predicate is used for each tree node remains random and the trees will be diverse.

  3. Specify how you want the model to be trained, by setting the Create trainer mode option.

    • Single Parameter: If you know how you want to configure the model, you can provide a specific set of values as arguments.

    • Parameter Range: If you are not sure of the best parameters, you can find the optimal parameters by using the Tune Model Hyperparameters component. You provide some range of values, and the trainer iterates over multiple combinations of the settings to determine the combination of values that produces the best result.

  4. For Number of decision trees, type the maximum number of decision trees that can be created in the ensemble. By creating more decision trees, you can potentially get better coverage, but training time increases.


    If you set the value to 1. However, only one tree can be produced (the tree with the initial set of parameters) and no further iterations are performed.

  5. For Maximum depth of the decision trees, type a number to limit the maximum depth of any decision tree. Increasing the depth of the tree might increase precision, at the risk of some overfitting and increased training time.

  6. For Minimum number of samples per leaf node, indicate the minimum number of cases that are required to create any terminal node (leaf) in a tree.

    By increasing this value, you increase the threshold for creating new rules. For example, with the default value of 1, even a single case can cause a new rule to be created. If you increase the value to 5, the training data would have to contain at least five cases that meet the same conditions.

  7. Select the Allow unknown values for categorical features option to create a group for unknown values in the training or validation sets. The model might be less precise for known values, but it can provide better predictions for new (unknown) values.

    If you deselect this option, the model can accept only the values that are contained in the training data.

  8. Attach a labeled dataset, and train the model:

    • If you set Create trainer mode to Single Parameter, connect a tagged dataset and the Train Model component.

    • If you set Create trainer mode to Parameter Range, connect a tagged dataset and train the model by using Tune Model Hyperparameters.


    If you pass a parameter range to Train Model, it uses only the default value in the single parameter list.

    If you pass a single set of parameter values to the Tune Model Hyperparameters component, when it expects a range of settings for each parameter, it ignores the values, and uses the default values for the learner.

    If you select the Parameter Range option and enter a single value for any parameter, that single value you specified is used throughout the sweep, even if other parameters change across a range of values.


After training is complete:

  • To save a snapshot of the trained model, select the Outputs tab in the right panel of the Train model component. Select the Register dataset icon to save the model as a reusable component.

  • To use the model for scoring, add the Score Model component to a pipeline.

Next steps

See the set of components available to Azure Machine Learning.