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(2001). Random forest. +Machine Learning, 45(1), 5–32. +https://doi.org/10.1023/a:1010933404324 + + + A framework for sensitivity analysis of +decision trees + Kamiński + Central European Journal of Operations +Research + 10.1007/s10100-017-0479-6 + 2017 + Kamiński, B., Jakubczyk, M., & +Szufel, P. (2017). A framework for sensitivity analysis of decision +trees. Central European Journal of Operations Research. +https://doi.org/10.1007/s10100-017-0479-6 + + + + + + From 4d7b5eec8ab99c07e812c79f1c415c2e7e09811d Mon Sep 17 00:00:00 2001 From: BotSci <63789325+botsci@users.noreply.github.com> Date: Mon, 7 Mar 2022 10:40:18 +0100 Subject: [PATCH 3/3] Creating 10.21105.test_journal.00029.jats --- .../10.21105.test_journal.00029.jats | 237 ++++++++++++++++++ 1 file changed, 237 insertions(+) create mode 100644 test_journal.00029/10.21105.test_journal.00029.jats diff --git a/test_journal.00029/10.21105.test_journal.00029.jats b/test_journal.00029/10.21105.test_journal.00029.jats new file mode 100644 index 0000000..1715eb2 --- /dev/null +++ b/test_journal.00029/10.21105.test_journal.00029.jats @@ -0,0 +1,237 @@ + + +

+ + + + +Journal of Open Source Software +JOSS + +2475-9066 + +Open Journals + + + +29 +10.21105/test_journal.00029 + +Nimbus: a Ruby gem to implement Random Forest algorithms +in a genomic selection context + + + +0000-0001-7699-3983 + +Bazán +Juanjo + + + + +0000-0002-9106-4063 + +Gonzalez-Recio +Oscar + + + + + +Departamento de Física Teórica. Universidad Autónoma de +Madrid. + + + + +Departamento de Mejora Genética Animal. Instituto Nacional +de Investigación y Tecnología Agraria y Alimentaria. + + + + +12 +7 +2017 + +7 +71 +29 + +Authors of papers retain copyright and release the +work under a Creative Commons Attribution 4.0 International License (CC +BY 4.0) +2022 +The article authors + +Authors of papers retain copyright and release the work under +a Creative Commons Attribution 4.0 International License (CC BY +4.0) + + + +random forest +genomics +machine learning +ruby +open science + + + + + + Summary +

Nimbus is a Ruby gem implementing Random Forest in a genomic + selection context, meaning every input file is expected to contain + genotype and/or phenotype data from a sample of individuals. Other + than the ids of the individuals, Nimbus handle the data as genotype + values for single-nucleotide polymorphisms (SNPs), so the variables in + the classifier must have values of 0, 1 or 2, corresponding with SNPs + classes of AA, AB and BB.

+

Nimbus provides a novel dataframe of random forest under ruby, and + implements a modified algorithm that can separate all genotypes for a + single marker, which can accommodate both additivity and dominance. + Further, it allows the user to specify a loss function and provide + full information of the trees in a .yml file.

+

Nimbus can be used to:

+ + +

Create a random forest using a training sample of individuals + with phenotype data.

+
+ +

Use an existent random forest to get predictions for a testing + sample.

+
+
+ + Random Forest +

The random forest algorithm is a classifier consisting in many + random decision trees, it was first proposed as a massively + non-parametric machine-learning algorithm by Leo Breiman + (Breiman, + 2001). It is based on choosing random subsets of variables + for each tree and using the most frequent, or the averaged tree + output as the overall classification. Random forest makes use of + bagging and randomization, constructing many decision trees + (Kamiński + et al., 2017) on bootstrapped samples of a given data set. + The prediction from the trees are averaged to make final + predictions.

+

In machine learning terms, it is an ensemble classifier, so it + uses multiple models to obtain better predictive performance than + could be obtained from any of the constituent models.

+

The forest outputs the class that is the mean or the mode (in + regression problems) or the majority class (in classification + problems) of the node’s output by individual trees.

+

The algorithm is robust to over-fitting and able to capture + complex interaction structures in the data, which may alleviate the + problems of analyzing genome-wide data.

+
+ + Learning algorithm +

Training: Each tree in the forest is constructed + using the following algorithm:

+ + +

Let the number of training cases be N, and the number of + variables (SNPs) in the classifier be M.

+
+ +

The mtry number of input variables is told to the algorithm + to be used in determining the decision at a node of the tree; m + should be much less than M (usually 1/3), and the optimal value + should be tuned for methods like grid search.

+
+ +

Choose a training set for this tree by drawing n samples with + replacement from all N available training cases (i.e. bootstrap + sampling). The rest of the cases (Out Of Bag sample) will be + used to estimate the error of the tree at predicting the classes + of this out of Bag samples.

+
+ +

For each node of the tree, randomly choose m SNPs on which to + base the decision at that node. Calculate the best split based + on these m SNPs in the training set.

+
+ +

Each tree is fully grown and not pruned (as may be done in + constructing a normal tree classifier).

+
+ +

When in a node there is not any SNP split that minimizes the + general loss function of the node, or the number of individuals + in the node is less than the minimum node size then label the + node with the average phenotype value of the individuals in the + node.

+
+
+

Testing: An independent sample can be pushed down + the tree to predict the most probable phenotype given the SNP + genotypes. It is assigned the label of the training sample in the + terminal node it ends up in. This procedure is iterated over all + trees in the ensemble, and the average vote of all trees is reported + as the random forest prediction.

+
+ + Nimbus +

Nimbus trains the algorithm based on an input file (learning + sample) containing the phenotypes of the individuals and their + respective list of genotype markers (i.e. SNPs). A random forest is + created and stored in a .yml file for future use.

+

Nimbus can also be run to make prediction in a validation set or + in a set of data containing yet to be observed response variable. In + this case, the predictions can be obtained using the random forest + created with a learning sample or using a previously stored random + forest.

+

If a learning sample is provided, the gem will create a file with + the variable importance of each feature (marker) in the data. The + higher the importance is, the more relevant the marker is to + correctly predict the response variable in new data.

+

Nimbus can be used for both classification or regression + problems, and the user may provide different parameter values in a + configuration file to tune the performance of the algorithm.

+

-

+
+
+ + + + + + + + BreimanLeo + + Random forest + Machine Learning + Springer Nature + 2001 + 45 + 1 + https://doi.org/10.1023/a:1010933404324 + 10.1023/a:1010933404324 + + + + + + KamińskiBogumił + JakubczykMichał + SzufelPrzemysław + + A framework for sensitivity analysis of decision trees + Central European Journal of Operations Research + Springer Nature + 201705 + https://doi.org/10.1007/s10100-017-0479-6 + 10.1007/s10100-017-0479-6 + + + + + +

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