Multiclass 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 forest algorithm. A decision forest is an ensemble model that rapidly builds a series of decision trees, while learning from tagged data.
More about decision forests
The decision forest algorithm is an ensemble learning method for classification. The algorithm works by building multiple decision trees and then voting on the most popular output class. Voting is a form of aggregation, in which each tree in a classification decision forest 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 have a greater weight in the final decision of the ensemble.
Decision trees in general are non-parametric models, meaning they support data with varied distributions. In each tree, a sequence of simple tests is run for each class, increasing the levels of a tree structure until a leaf node (decision) is reached.
Decision trees have many advantages:
- They can represent non-linear decision boundaries.
- They are efficient in computation and memory usage during training and prediction.
- They perform integrated feature selection and classification.
- They are resilient in the presence of noisy features.
The decision forest classifier in Azure Machine Learning consists of an ensemble of decision trees. Generally, ensemble models provide better coverage and accuracy than single decision trees. For more information, see Decision trees.
How to configure Multiclass Decision Forest
Add the Multiclass Decision Forest component to your pipeline in the designer. You can find this component under Machine Learning, Initialize Model, and Classification.
Double-click the component to open the Properties pane.
For Resampling method, choose the method used to create the individual trees. You can choose from bagging or replication.
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. 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, creating diverse trees.
Specify how you want the model to be trained, by setting the Create trainer mode option.
Single Parameter: Select this option if you know how you want to configure the model, and provide a set of values as arguments.
Parameter Range: Select this option if you are not sure of the best parameters, and want to run a parameter sweep. Select a range of values to iterate over, and the Tune Model Hyperparameters iterates over all possible combinations of the settings you provided to determine the hyperparameters that produce the optimal results.
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 might increase.
If you set the value to 1; however, this means that only one tree can be produced (the tree with the initial set of parameters), and no further iterations are performed.
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.
Number of random splits per node: Type the number of splits to use when building each node of the tree. A split means that features in each level of the tree (node) are randomly divided.
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.
Connect 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.
Submit the pipeline.
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