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The comparative analysis of the proposed classifiers has demonstrated that the best results are achieved by a decision forest made of 30 trees.
The algorithm selection is made by a decision forest composed of several trees on the basis of the values of a set of heterogeneous features.
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The models were developed by using a novel QSAR method, Decision Forest, which combines the results of multiple heterogeneous but comparable Decision Tree models to produce a consensus prediction.
In the baseline recognition system, the learning task is accomplished by support vector machine [45], decision forest, and ridge regression algorithms.
We present a novel classification method, decision forest (DF), for class prediction using omics data.
The approach started with identification of multiple disease pathways, called a gene forest, in which the genes extracted from the decision forest constructed by supervised learning of the genome-wide transcriptional profiles for patients and normal samples.
Inspired from deep neural decision forest [66] and random forest [42], Transfer-NDF uses neural networks as decision trees as to build a forest of neural decision trees.
We were robbed by a decision.
An approach to exploring data and decisions made by a random forest is also presented.
It is trained by growing a forest of decision trees using CART methodology.
In Section 2.1, we will introduce the random forest classifier by describing the training of a decision tree and then explain how this tree is used in the random forest.
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