After the first split, the decision tree algorithm examines each of the two subsets of data and finds a predictor variable and a value that gives the most information. The process continues until a ...

Four years of research led to a specific decision tree data mining algorithm yielding best results. Results obtained from the BC database were excellent, revealing $4.7 billion ...

The system is structured around a client-server architecture designed to provide scalability, remote accessibility, and ...

Starting with all 200 training items, the decision tree algorithm scans the data and finds the one value of the one predictor variable that splits the data into two sets in such a way that the most ...

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 ...

We now look at how information gain can be used in practice in an algorithm to construct decision trees. The ID3 algorithm; The calculation for information gain is the most difficult part of this ...

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 ...

An innovative algorithm called Spectral Expansion Tree Search helps autonomous robotic systems make optimal choices on the move. In 2018, Google DeepMind's AlphaZero program taught itself the ...

But there is also some empirical work comparing various algorithms across many datasets and drawing some conclusions, what types of problems tend to do better with trees vs logistic regression.