课程: Machine Learning and AI Foundations: Decision Trees with KNIME

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Accuracy

Accuracy - KNIME教程

课程: Machine Learning and AI Foundations: Decision Trees with KNIME

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Accuracy

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- [Instructor] Okay, it's time to get an assessment of how accurate our regression tree is. To do that, we have to be downstream of the predictor because we have to involve cases that were predicted but that were not fed to the learner. In other words, in terms of partitioning, we need both our training data, as well as our 20% test, being fed to the score so that we can see how good a job we did on what is sometimes called the unseen data. So let's go in here and configure, and we're going to need to fix this here. We want our predicted column to be Prediction mpg, but we want our reference column to be miles per gallon. And we're going to click on OK and Execute an Open Views. Now, I know that's a bit small, probably, but we can see that we have our R squared, and we also have our root mean square error. If you're doing regression tree, you're probably also trying regression itself, so I would probably focus on the R…

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