Sentence examples for decision tree learning from inspiring English sources

A decision tree learning algorithm such as C4.5 usually considers one variable at a time and ignores interdependencies among input attributes, which reduces its model accuracy.

Using decision tree learning techniques, this work proposes a straightforward and effective model to detect the mean shifts in multivariate control charts.

In this paper, we demonstrate the application of a decision tree learning algorithm for designing pedestrian landscapes that encourage walking for health.

The approach presented in this paper makes use of these logs (i) to extract the learning flow structure using process mining, and (ii) to obtain the underlying rules that control the adaptive learning of students by means of decision tree learning.

In [64], the authors used decision tree learning to find the optimal wideband spectrum-sensing order.

There are other parallel decision tree learning algorithms [19, 21, 22] that work by clubbing data and task parallelism.

A nonparametric decision-tree learning algorithm identified sets of 12-month predictors of developmental status at 24 months.

We used the C5.0 decision-tree learning algorithm.

We explored physicians' prescriptions for each of these profiles using C5.0 decision-tree learning algorithm.

We applied the C5.0 decision-tree learning algorithm to the entire database to extract rules that explained physicians' prescriptions on the basis of patient variables.

To facilitate the generation of rules by decision-tree learning algorithms, we formalized the prescriptions by implementing a typology model for drug therapy as described previously [ 12].