The list of parameters of the modified data set for machine learning (<i>n</i> = 162).

The list of parameters of the modified data set for machine learning (<i>n</i> = 162).

<p>The list of parameters of the modified data set for machine learning (<i>n</i> = 162).</p>

محفوظ في:

التفاصيل البيبلوغرافية
المؤلف الرئيسي:	Uğur Ejder (22683228) (author)
مؤلفون آخرون:	Pınar Uskaner Hepsağ (22683231) (author)
منشور في:	2025
الموضوعات:	Biotechnology Ecology Cancer Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified small sample size lr &# 8211 evaluated using 5 assisted reproductive technologies artificial bee colony art ), yet address class imbalance support vector machine pharmaceutical supplement use enhance predictive performance abc hybrids outperformed abc hybrid counterparts local interpretable model abc hybrid model model performance supplement variables producing interpretable dietician support vitro fertilization synthetic minority studies rely sampling technique retrospective dataset regression tree random forest observed improvements nearest neighbors limited optimization influential features individual predictions improving prediction implemented alongside future studies four algorithms folic acid fold cross exploratory rather dietary data conventional algorithms concept study clinically directive binary representation baseline models algorithm models agnostic explanations accuracy ). 21 predictors
الوسوم:	إضافة وسم لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!

مواد مشابهة

The statistical description of the original data set of the patients (<i>n</i> = 162).
حسب: Uğur Ejder (22683228)
منشور في: (2025)

Top-performing model configuration and performance by algorithm type.
حسب: Uğur Ejder (22683228)
منشور في: (2025)

Comparison of baseline and hybrid machine learning models in predicting IVF outcomes (%).
حسب: Uğur Ejder (22683228)
منشور في: (2025)

Confusion matrix of the ABC–LR–RF hybrid model for IVF outcome prediction.
حسب: Uğur Ejder (22683228)
منشور في: (2025)

ROC and PR–AUC curves of the ABC–LR–RF hybrid model for IVF outcome prediction.
حسب: Uğur Ejder (22683228)
منشور في: (2025)

The most effective active substances for subfertility treatment.
حسب: Uğur Ejder (22683228)
منشور في: (2025)

The comparison of the accuracy score of the benchmark and the proposed models.
حسب: Uğur Ejder (22683228)
منشور في: (2025)

The prediction accuracy of simple versus hybrid models.
حسب: Uğur Ejder (22683228)
منشور في: (2025)

The accuracy score change matrix following model development.
حسب: Uğur Ejder (22683228)
منشور في: (2025)

Calibration curve of the ABC–LR–RF hybrid model for IVF outcome prediction.
حسب: Uğur Ejder (22683228)
منشور في: (2025)

Flow diagram of the proposed model.
حسب: Uğur Ejder (22683228)
منشور في: (2025)

Flow diagram of the conversion of drugs into active substances.
حسب: Uğur Ejder (22683228)
منشور في: (2025)

Hospitals by region in Brazil, with a list of hospitals.
حسب: Roberta Moreira Wichmann (14259316)
منشور في: (2025)

List of variables used to predict hospitalization mortality in the federated and local learning approaches.
حسب: Roberta Moreira Wichmann (14259316)
منشور في: (2025)

Comparison of predictive performance between federated and local learning models.
حسب: Roberta Moreira Wichmann (14259316)
منشور في: (2025)

Schematic visualization of the federated learning approaches.
حسب: Roberta Moreira Wichmann (14259316)
منشور في: (2025)

Regional analysis of socioeconomic indicators and federated learning performance gain.
حسب: Roberta Moreira Wichmann (14259316)
منشور في: (2025)

Raw performance metrics for the Random Forest (RF) model.
حسب: Roberta Moreira Wichmann (14259316)
منشور في: (2025)

Raw performance metrics for the MLP model.
حسب: Roberta Moreira Wichmann (14259316)
منشور في: (2025)

Raw performance metrics for the Logistic Regression (LR) model.
حسب: Roberta Moreira Wichmann (14259316)
منشور في: (2025)

Evolution of the hyperparameter <i>t</i> in relation to the LR and MLP models.
حسب: Roberta Moreira Wichmann (14259316)
منشور في: (2025)

Data for "Long-term surveillance of a woodland salamander community with a review of long-term field studies in plethodontids"
حسب: Richard Lehtinen (12908108)
منشور في: (2025)

Category-level summary of genes with potentially distinct functions between Udai21 and Koshihikari.
حسب: Hiroki Ikeda (510280)
منشور في: (2025)

Statistics for Udai21 pseudomolecule sequences.
حسب: Hiroki Ikeda (510280)
منشور في: (2025)

Genome assembly and predicted gene BUSCO scores.
حسب: Hiroki Ikeda (510280)
منشور في: (2025)

Genome assembly statistics for Udai21.
حسب: Hiroki Ikeda (510280)
منشور في: (2025)

Classification of Udai21 and the World Rice Core Collection based on organelle genome data.
حسب: Hiroki Ikeda (510280)
منشور في: (2025)

Classification of Udai21 and the World Rice Core Collection based on whole genome sequencing data.
حسب: Hiroki Ikeda (510280)
منشور في: (2025)

Rice chromosome SNP density for five rice varieties.
حسب: Hiroki Ikeda (510280)
منشور في: (2025)

Principal component analysis of Udai21 and the World Rice Core Collection.
حسب: Hiroki Ikeda (510280)
منشور في: (2025)

Classification of Udai21 and the World Rice Core Collection based on ddRAD-Seq data.
حسب: Hiroki Ikeda (510280)
منشور في: (2025)

Genes that functions were potentially distincted between Udai21 and Koshihikari.
حسب: Hiroki Ikeda (510280)
منشور في: (2025)

Genes with high and moderate impact variants between Udai21 and Koshihikari.
حسب: Hiroki Ikeda (510280)
منشور في: (2025)

Impact damage assessment of sandwich composite materials using non-destructive techniques
حسب: Tendai Chipanga (22571069)
منشور في: (2025)

DATASET: Data-Driven Clinical Decision Trees and Generative Topographic Mapping to Guide Stroke Rehabilitation Program Allocation
حسب: Dimitrios-Sokratis Komaris (10266358)
منشور في: (2025)

Resource and Environmental Carrying Capacity Assessment and Forecast Dataset and Code for Xuzhou City, China (2011–2023)
حسب: 楠姜 (21426950)
منشور في: (2025)

Comparison of hierarchical classifier performance among four machine learning models for the early-loss, intermediate-loss, and late/no-loss groups.
حسب: Raiki Yoshimura (22676573)
منشور في: (2025)

Comparison of multi-class classifier performance among four machine learning models for early-loss, intermediate-loss, and late/no-loss groups.
حسب: Raiki Yoshimura (22676573)
منشور في: (2025)

Comparison of binary classifier performance among four machine learning models for early-loss, intermediate-loss, and late/no-loss groups.
حسب: Raiki Yoshimura (22676573)
منشور في: (2025)

Comparison of performance of four machine learning models for long-term graft loss prediction.
حسب: Raiki Yoshimura (22676573)
منشور في: (2025)

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