Full model selection using regression trees for numeric predictions of biomarkers for metabolic challenges in dairy cows

Reyes, J.; Walleser, E.; Hachenberg, Sabrina; Gruber, Simone; Kammer, Martin; Baumgartner, Christian; Mansfeld, Rolf; Anklam, Kelly; Döpfer, Dörte

doi:10.1016/j.prevetmed.2021.105422

Cited by 5 publications

(2 citation statements)

References 26 publications

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“…With a FMS [25,26,24] strategy, however, we are able to identify the choices that yield the highest measure of performance. More details on the FMS models can be found in Methods.…”

Section: Identifying General Practices To Predict Potato Disease From...mentioning

confidence: 99%

“…FMS [25,26,24] involves the process of listing all data preprocessing steps, model options and selection of predictors, and using a decision tree model to identify the choices that yield the highest measure of performance. Here, we fit a FMS strategy with the following options: 1) type of normalization, 2) type of zero replacement, 3) taxonomic level, and 4) data augmentation.…”

Section: Full Model Selectionmentioning

confidence: 99%

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Human limits in Machine Learning: Prediction of plant phenotypes using soil microbiome data

Aghdam,

Tang,

Shan

et al. 2024

Preprint

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The preservation of soil health is a critical challenge in the 21st century due to its significant impact on agriculture, human health, and biodiversity. We provide the first deep investigation of the predictive potential of machine learning models to understand the connections between soil and biological phenotypes. We investigate an integrative framework performing accurate machine learning-based prediction of plant phenotypes from biological, chemical, and physical properties of the soil via two models: random forest and Bayesian neural network. We show that prediction is improved when incorporating environmental features like soil physicochemical properties and microbial population density into the models, in addition to the microbiome information. Exploring various data preprocessing strategies confirms the significant impact of human decisions on predictive performance. We show that the naive total sum scaling normalization that is commonly used in microbiome research is not the optimal strategy to maximize predictive power. Also, we find that accurately defined labels are more important than normalization, taxonomic level or model characteristics. In cases where humans are unable to classify samples accurately, machine learning model performance is limited. Lastly, we provide domain scientists via a full model selection decision tree to identify the human choices that optimize model prediction power.

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“…With a FMS [25,26,24] strategy, however, we are able to identify the choices that yield the highest measure of performance. More details on the FMS models can be found in Methods.…”

Section: Identifying General Practices To Predict Potato Disease From...mentioning

confidence: 99%

Section: Full Model Selectionmentioning

confidence: 99%