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Deprecated**Decisions Tree Ensembles for KNIME** version **4.1.0.v201911271250** by **KNIME AG, Zurich, Switzerland**

Learns a random forest* (an ensemble of decision trees) for regression. Each of the regression tree models is learned on a different set of rows (records) and/or a different set of columns (describing attributes), whereby the latter can also be a bit-vector or byte-vector descriptor (e.g. molecular fingerprint). The output model describes an ensemble of regression tree models and is applied in the corresponding predictor node using a simply mean of the individual predictions.

For a more general description and suggested default parameters see the node description of the classification
*Random Forest Learner* node.

This node provides a subset of the functionality of the *Tree Ensemble Learner (Regression)*. If you need additional
functionality, please check out the *Tree Ensemble Learner (Regression)*

(*) RANDOM FORESTS is a registered trademark of Minitab, LLC and is used with Minitab’s permission.

- Target Column
- Select the column containing the value to be learned. Rows with missing values in this column will be ignored during the learning process.
- Attribute Selection
Select the attributes to use learn the model. Two variants are possible.

*Fingerprint attribute*uses the different bit/byte positions in the selected bit/byte vector as learning attributes (for instance a bit vector of length 1024 is expanded to 1024 binary attributes or 1024 long byte vector is expanded to the corresponding number of numeric attributes). All vectors in the selected column must have the same length.*Column attributes*are nominal and numeric columns used as descriptors. Numeric columns are split in a <= fashion; nominal columns are currently split by creating child nodes for each of the values.- Enable Hightlighting (#patterns to store)
- If selected, the node stores the selected number of rows and allows highlighting them in the node view.

- Limit number of levels (tree depth)
- Number of tree levels to be learned. For instance, a value of 1 would only split the (single) root node (decision stump).
- Minimum node size
- Minimum number of records in child nodes. It can be at most half of the minimum split node size (see above). Note, this parameter is currently ignored for nominal splits.

- Number of models
- The number of decision trees to learn. A "reasonable" value can range from very few (say 10) to many thousands for small data sets with few target category values.
- Use static random seed
- Choose a seed to get reproducible results.

- The data to learn from. It must contain at least one numeric target column and either a fingerprint (bit-vector/byte-vector) column or another numeric or nominal column.

- The input data with the out-of-bag response estimates, i.e. for each input row the mean output of all models that
did not use the row in the training. If the entire data was used to train the individual models then this
output will contain the input data with missing response and response variance values. The appended columns are
equivalent to the columns appended by the corresponding predictor node. There is one additional column
*model count*, which contains the number of models used for the voting (number of models not using the row throughout the learning.) - A statistics table on the attributes used in the different tree learners. Each row represents one training
attribute with these statistics:
*#splits (level x)*as the number of models, which use the attribute as split on level*x*(with level 0 as root split);*#candidates (level x)*is the number of times an attribute was in the attribute sample for level*x*(in a random forest setup these samples differ from node to node). If no attribute sampling is used*#candidates*is the number of models. Note, these numbers are uncorrected, i.e. if an attribute is selected on level 0 but is also in the candidate set of level 1 (but will not be split on level 1 because it has been split one level up), the #candidate number will still count the attribute as candidate. - The trained model.

- Tree Views
- An decision tree viewer for all the trained models. Use the spinner to iterate through the different models.

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~~Table Column to Variable~~(3 %) Deprecated- CSV Reader (3 %)
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- Missing Value (3 %)
- Shuffle (3 %)
- Cache (3 %)
- Boosting Learner Loop End (3 %)
- Boosting Learner Loop Start (3 %)
~~XLS Reader~~(3 %) Deprecated- RDKit Fingerprint (3 %) Streamable
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~~Tree Ensemble Model Extract~~(2 %) Deprecated~~Tree Ensemble Predictor~~(2 %) StreamableDeprecated~~Random Forest Predictor~~(2 %) Deprecated~~Linear Correlation~~(2 %) Deprecated~~Double To Int~~(2 %) StreamableDeprecated- Column Filter (2 %) Streamable
- Column Splitter (2 %) Streamable
- Single sample t-test (2 %)
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- Model Loop End (2 %)
- Boosting Learner Loop End (2 %)
- Model to Cell (2 %)
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To use this node in KNIME, install KNIME Core from the following update site:

KNIME 4.1

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