nestedcv: an R package for fast implementation of nested cross-validation with embedded feature selection designed for transcriptomics and high-dimensional data.
nestedcv: an R package for fast implementation of nested cross-validation with embedded feature selection designed for transcriptomics and high-dimensional data.
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DOI：
10.1093/bioadv/vbad048
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发表时间：
2023
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虽然机器学习模型通常用于医学研究，但许多分析都将其简单划分为训练数据和保持测试数据，并使用交叉验证（CV）来调整模型超参数。具有嵌入式特征选择的嵌套CV特别适合于样本大小经常有限的生物医学数据，但预测因子的数量可能会显着增加（P n）。nestedcv R包通过glmnet包为lasso和弹性网络正则化线性模型实现了完全嵌套的k × l-fold CV，并通过插入符号框架支持大量其他机器学习模型。内部CV用于调整模型，外部CV用于确定无偏倚的模型性能。提供了用于特征选择的快速过滤器功能，并且该包确保过滤器嵌套在外部CV循环内，以避免性能测试集的信息泄漏。通过外部CV进行的性能测量也用于使用马蹄先验参数来实现贝叶斯线性和逻辑回归模型，以鼓励稀疏模型并确定无偏模型准确度。R包nestedcv可从CRAN：https://CRAN.R-project.org/package=nestedcv获得。
Although machine learning models are commonly used in medical research, many analyses implement a simple partition into training data and hold-out test data, with cross-validation (CV) for tuning of model hyperparameters. Nested CV with embedded feature selection is especially suited to biomedical data where the sample size is frequently limited, but the number of predictors may be significantly larger (P ≫ n). The nestedcv R package implements fully nested k × l-fold CV for lasso and elastic-net regularized linear models via the glmnet package and supports a large array of other machine learning models via the caret framework. Inner CV is used to tune models and outer CV is used to determine model performance without bias. Fast filter functions for feature selection are provided and the package ensures that filters are nested within the outer CV loop to avoid information leakage from performance test sets. Measurement of performance by outer CV is also used to implement Bayesian linear and logistic regression models using the horseshoe prior over parameters to encourage a sparse model and determine unbiased model accuracy. The R package nestedcv is available from CRAN: https://CRAN.R-project.org/package=nestedcv.
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影响因子：
14.9
作者：
Zeng S;Mao Z;Ren Y;Wang D;Xu D;Joshi T
通讯作者：
Joshi T
影响因子：
3
作者：
Habier D;Fernando RL;Kizilkaya K;Garrick DJ
通讯作者：
Garrick DJ
DOI：
10.1093/g3journal/jkac226
发表时间：
2022-11-04
期刊：
G3 (Bethesda, Md.)
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5.6
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BGLR统计包的全基因组回归和预测。
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3.3
作者：
Pérez P;de los Campos G
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de los Campos G