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Ryan Curtin
2023-09-25 13:18:43 -04:00
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doc/user/methods/decision_tree.md
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@@ -15,7 +15,7 @@ constructor parameters that can be used to control the behavior of the tree.
|---------------|-----------------|
| `DecisionTree(numClasses)` | Initialize tree without training. |
| `DecisionTree(data, labels, numClasses)` | Train on numerical-only data. |
| ```DecisionTree(data, labels, numClasses, minimumLeafSize,<br>minimumGainSplit, maximumDepth)``` | Train on numerical-only data with hyperparameters. |
| <code>DecisionTree(data, labels, numClasses, minimumLeafSize,<br>minimumGainSplit, maximumDepth)</code> | Train on numerical-only data with hyperparameters. |
| `DecisionTree(data, datasetInfo, labels, numClasses)` | Train on mixed categorical data. |
| `DecisionTree(data, datasetInfo, labels, numClasses, minimumLeafSize, minimumGainSplit, maximumDepth)` | Train on mixed categorical data with hyperparameters. |
| `DecisionTree(data, datasetInfo, labels, numClasses, weights)` | Train on weighted mixed categorical data. |