Information gain tends to favor attributes with more categories/specific values, because it is not adjusted for the number of possible distinct values. You can see that chasing higher accuracies is often just a waste of time because it’s not the accuracy that’s most important, but rather the business impact that a model has. This question is very domain and dataset specific. In some fields that would be considered great and used with no problem, while in other fields it would be horrible. Decision Tree Algorithm: Percentage Accuracy. If you want to maximize your tree for accuracy, you can select accuracy directly as the main criterion for tree growth. But it is not possible to say in the abstract whether accuracy of 70% is "good enough" for prediction. the proposed model achieved 99.81 accuracy for predicting the species of the mushrooms edible or poisonous. METHODOLOGY In this paper the RapidMiner Studio 68 was used to perform experiments by taking the past. The ratio of these forms the basis of the confidence score generated by the DT. A decision tree is a tree like collection of nodes intended to create a decision on values affiliation to a class or an estimate of a numerical target value.
The blue/red labels under each node indicate the number of examples that fell into each category in that node. Decision Tree (Concurrency) Synopsis This Operator generates a decision tree model, which can be used for classification and regression. Hi there, you have a few questions embedded in your post, so I'll try to comment on most of them.
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