"Jump discontinuities" in visual plots led to use of data mining decision trees as an ideal form of analysis useful in obtaining a profit exploration pattern from the British Columbia database.

Evolutionary algorithms have emerged as a robust alternative to traditional greedy approaches for decision tree induction. By mimicking the natural selection process, these algorithms iterate over ...

The Data Science Lab Multi-Class Classification Using a scikit Decision Tree Decision trees are useful for relatively small datasets that have a relatively simple underlying structure, and when the ...

A decision tree is a machine learning technique that can be used for binary classification or multi-class classification. A binary classification problem is one where the goal is to predict the value ...

Boosted decision trees Physicists have been using decision trees since the 1970s. Decision-tree algorithms work by running data through a series of decision points. At each point, the algorithm ...

Conclusions: Multiple molecular and clinicopathological variable integrated decision tree algorithms may individually predict the recurrence pattern for NPC. This decision tree algorism provides a ...

Missing values may be easier to manage with decision trees than they are with other classification methods. 33 The tree-building algorithm in the Salford System CART software uses a method of ...

The authors’ approach is based on differences between assembly op-code frequencies in malware and benign classes. They have also utilized decision tree algorithms to simplify the classification.