The second was classification (also known as classification tree or decision tree), which can be used to create an actual branching tree to predict the output value of an unknown data point.
Examples of artifacts from BRM tools include rule sets, decision tables, decision trees, and score CARDS.
As a perfect quantitative analysis method the decision tree analysis method is practicable and simple and realize scientific decision in bid invitation.
When analyze financial factor, decision tree method on the basis of inductive reasoning means is adopted to analyze the infection of financial factor to loan risk classification.
Decision tree algorithms are applied to the data mining of the mammography classification, proposes a medical images classifier based on decision tree algorithm, the experiment results are given.
Land cover map of classification was generated for major biological conservation areas in Yunnan province using MODIS data and decision tree joined maximum likelihood classifications.
Up to now, the decision tree method is used to find the optimal solution of a Bayesian decision problem, that is, to select an optimal one from several decision alternatives.
And we're going to do this initially by looking at a straightforward implementation based upon what's called the decision tree.
A context-dependent polyphone disambiguation technology was proposed, combined with the decision tree and maximum entropy methods, the accuracy can be improved to 99.82%.
Now, the decision tree, if I branch left, it's a binary tree.
Make a decision tree for yourself.
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