Overview

The Decision Tree Regression Forecast Processor generates a forecast for a numerical dependent variable based on a learned decision tree.

In the decision tree algorithm, the original input data is split into various partitions. The impurity measure splits the data by creating homogeneous data points within one node with regard to the output variable.

The dependent variable is predicted using the trained dataset represented by the regression tree. More specifically, this prediction is extracted from the leaves carrying the information of both the independent variable's range (interval, set of values) and the dependent variable's assigned prediction.

Input

The processor requires two input datasets. The first input port (the one on the left) corresponding to the training dataset (this data should be already labeled). The second input port (the one on the right) corresponding to the test dataset.

It goes without saying that the training and the test datasets should have the same schema. 

Configuration

The last parameter (Handling of unseen categorical features) has three options:

• KEEP: creates one new category for all unseen values
• ERROR: fails if unseen values occur
• SKIP: ignores the unseen values

Output

The Decision Tree Classification Processor provides two different outputs:

• A decision tree: Based on the training dataset. This tree can be accessed through the decision tree classification processor under the tab "Results".
• A forecast table: The test dataset with the added "prediction" column. It can be viewed via the result table linked to the processor's output.

Keep in mind that, just because no errors are shown, that doesn't mean the regression is reasonable. It depends on the choice of the independent variables.

Example

In this example, the input dataset represents information about train passengers (Name, Class, Age...). The goal is to build a decision tree that estimates the passenger's travelling class using the fare and sex attributes.  

Example Input

We used a Horizontal Split Processor to split the input dataset (418 entries) into two different datasets: The training dataset containing 80% of the input data (334 entries) and the Test dataset containing the remaining 20% (84 entries).

Training Dataset

Test Dataset

Workflow

As mentioned above, the Horizontal split processor is used to split the original data into a training dataset (80%) and a test dataset (20%). The column Selection is used to select the dependent and independent variables out of the given result. As the predicted class is of type float we use the data type conversion processor to convert the outputted values to the type integer (we use the rounding strategy DOWN).

Example Configuration

Result

decision tree

Result table

Note that the predicted labels don't exactly match the actual labels. With that being said a training data with 400 entries isn't sufficient. The training dataset needs to be of a considerable size in order to have a more accurate result and reduce the error ratio.

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