Alexandr Shapkin created IGNITE-20139: -----------------------------------------
Summary: RandomForestClassifierTrainer is checking the same conditions Key: IGNITE-20139 URL: https://issues.apache.org/jira/browse/IGNITE-20139 Project: Ignite Issue Type: Bug Components: ml Affects Versions: 2.15 Reporter: Alexandr Shapkin Attachments: TreeSample2_Portfolio_Change.png, random-forest.zip We tried to use GridGain's machine learning capabilities, and discovered a bug in GG's implementation of Random Forest. When comparing GG's output with python prototype (scikit-learn lib), we noticed that GG's predictions have much lower accuracy despite using the same data set and model parameters. Further investigation showed that GridGain generates decision trees that kinda "loop". The tree starts checking the same condition over and over until it reaches the maximum tree depth. I've attached a standalone reproducer which uses a small excerpt of our data set. It loads data from the csv file, then performs the training of the model for just 1 tree. Then the reproducer finds one of the looping branches and prints it. You will see that every single node in the branch uses the same feature, value and has then same calculated impurity. On my machine the code reproduces this issue 100% of time. I've also attached an example of the tree generated by python's scikit-learn on the same data set with the same parameters. In python the tree usually doesn't get deeper than 20 nodes. -- This message was sent by Atlassian Jira (v8.20.10#820010)