May 2020
Intermediate to advanced
530 pages
17h 8m
English
Content preview from Hands-On Machine Learning with C++
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Using random forest with Shark-ML
The random forest algorithm in the Shark-ML library is located in the RFClassifier class, and the corresponding trainer is located in the RFTrainer class. We use the original dataset values without preprocessing for the random forest algorithm implementation. First, we configure the trainer for this type of classifier. These are the next methods for configuration:
- setNTrees: Set the number of trees.
- setMinSplit: Set the minimum number of samples that are split.
- setMaxDepth: Set the maximum depth of the tree.
- setNodeSize: Set the maximum node size when the node is considered pure.
- minImpurity: Set the minimum impurity level below which a node is considered pure.
After we configure the trainer object, we ...
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