Configuration

XGBoost (log scran)

XGBoost is a gradient boosting decision tree model that learns multiple tree structures in the form of a series of input features and their values, leading to a prediction decision, and averages predictions from all its trees. Here, input features are normalised gene expression values.

24 evaluations · 24 results

Overview

An explanatory profile has not yet been reviewed for this record.

Consult the linked sources for architecture or protocol details. Missing evidence is not evidence of a missing capability.

Evaluations and results

24 evaluations · 24 results. Different protocols are not a single leaderboard.

Sorted by Accuracy (higher is better). The best value in each column is highlighted. Decimals are rounded for display; each value links to the printed value and its source.

Tested configuration	Protocol and dataset	Accuracy	Macro F1	Weighted F1
XGBoost (log scran)Evaluation	Open Problems label projection PANCREAS-RANDOM-ACCURACY: Label projection on Pancreas (random split), Accuracy, Pancreas (random split) (Open Problems label projection split)	0.969 (best in column)	Not reported	Not reported
XGBoost (log scran)Evaluation	Open Problems label projection PANCREAS-RANDOM-LABEL-NOISE-ACCURACY: Label projection on Pancreas (random split with label noise), Accuracy, Pancreas (random split with label noise) (Open Problems label projection split)	0.953	Not reported	Not reported
XGBoost (log scran)Evaluation	Open Problems label projection PANCREAS-BATCH-ACCURACY: Label projection on Pancreas (by batch), Accuracy, Pancreas (by batch) (Open Problems label projection split)	0.934	Not reported	Not reported
XGBoost (log scran)Evaluation	Open Problems label projection TABULA-MURIS-SENIS-LUNG-RANDOM-ACCURACY: Label projection on Tabula Muris Senis Lung (random split), Accuracy, Tabula Muris Senis Lung (random split) (Open Problems label projection split)	0.865	Not reported	Not reported
XGBoost (log scran)Evaluation	Open Problems label projection CENGEN-BATCH-ACCURACY: Label projection on CeNGEN (split by batch), Accuracy, CeNGEN (split by batch) (Open Problems label projection split)	0.83	Not reported	Not reported
XGBoost (log scran)Evaluation	Open Problems label projection CENGEN-RANDOM-ACCURACY: Label projection on CeNGEN (random split), Accuracy, CeNGEN (random split) (Open Problems label projection split)	0.821	Not reported	Not reported
XGBoost (log scran)Evaluation	Open Problems label projection ZEBRAFISH-RANDOM-ACCURACY: Label projection on Zebrafish (random split), Accuracy, Zebrafish (random split) (Open Problems label projection split)	0.788	Not reported	Not reported
XGBoost (log scran)Evaluation	Open Problems label projection ZEBRAFISH-LABS-ACCURACY: Label projection on Zebrafish (by laboratory), Accuracy, Zebrafish (by laboratory) (Open Problems label projection split)	0.236	Not reported	Not reported
XGBoost (log scran)Evaluation	Open Problems label projection CENGEN-BATCH-F1: Label projection on CeNGEN (split by batch), F1 score, CeNGEN (split by batch) (Open Problems label projection split)	Not reported	Not reported	0.842
XGBoost (log scran)Evaluation	Open Problems label projection CENGEN-BATCH-F1-MACRO: Label projection on CeNGEN (split by batch), Macro F1 score, CeNGEN (split by batch) (Open Problems label projection split)	Not reported	0.472	Not reported
XGBoost (log scran)Evaluation	Open Problems label projection CENGEN-RANDOM-F1: Label projection on CeNGEN (random split), F1 score, CeNGEN (random split) (Open Problems label projection split)	Not reported	Not reported	0.826
XGBoost (log scran)Evaluation	Open Problems label projection CENGEN-RANDOM-F1-MACRO: Label projection on CeNGEN (random split), Macro F1 score, CeNGEN (random split) (Open Problems label projection split)	Not reported	0.766	Not reported
XGBoost (log scran)Evaluation	Open Problems label projection PANCREAS-BATCH-F1: Label projection on Pancreas (by batch), F1 score, Pancreas (by batch) (Open Problems label projection split)	Not reported	Not reported	0.934
XGBoost (log scran)Evaluation	Open Problems label projection PANCREAS-BATCH-F1-MACRO: Label projection on Pancreas (by batch), Macro F1 score, Pancreas (by batch) (Open Problems label projection split)	Not reported	0.788	Not reported
XGBoost (log scran)Evaluation	Open Problems label projection PANCREAS-RANDOM-F1: Label projection on Pancreas (random split), F1 score, Pancreas (random split) (Open Problems label projection split)	Not reported	Not reported	0.969 (best in column)
XGBoost (log scran)Evaluation	Open Problems label projection PANCREAS-RANDOM-F1-MACRO: Label projection on Pancreas (random split), Macro F1 score, Pancreas (random split) (Open Problems label projection split)	Not reported	0.919 (best in column)	Not reported
XGBoost (log scran)Evaluation	Open Problems label projection PANCREAS-RANDOM-LABEL-NOISE-F1: Label projection on Pancreas (random split with label noise), F1 score, Pancreas (random split with label noise) (Open Problems label projection split)	Not reported	Not reported	0.953
XGBoost (log scran)Evaluation	Open Problems label projection PANCREAS-RANDOM-LABEL-NOISE-F1-MACRO: Label projection on Pancreas (random split with label noise), Macro F1 score, Pancreas (random split with label noise) (Open Problems label projection split)	Not reported	0.796	Not reported
XGBoost (log scran)Evaluation	Open Problems label projection TABULA-MURIS-SENIS-LUNG-RANDOM-F1: Label projection on Tabula Muris Senis Lung (random split), F1 score, Tabula Muris Senis Lung (random split) (Open Problems label projection split)	Not reported	Not reported	0.864
XGBoost (log scran)Evaluation	Open Problems label projection TABULA-MURIS-SENIS-LUNG-RANDOM-F1-MACRO: Label projection on Tabula Muris Senis Lung (random split), Macro F1 score, Tabula Muris Senis Lung (random split) (Open Problems label projection split)	Not reported	0.817	Not reported
XGBoost (log scran)Evaluation	Open Problems label projection ZEBRAFISH-LABS-F1: Label projection on Zebrafish (by laboratory), F1 score, Zebrafish (by laboratory) (Open Problems label projection split)	Not reported	Not reported	0.298
XGBoost (log scran)Evaluation	Open Problems label projection ZEBRAFISH-LABS-F1-MACRO: Label projection on Zebrafish (by laboratory), Macro F1 score, Zebrafish (by laboratory) (Open Problems label projection split)	Not reported	0.198	Not reported
XGBoost (log scran)Evaluation	Open Problems label projection ZEBRAFISH-RANDOM-F1: Label projection on Zebrafish (random split), F1 score, Zebrafish (random split) (Open Problems label projection split)	Not reported	Not reported	0.784
XGBoost (log scran)Evaluation	Open Problems label projection ZEBRAFISH-RANDOM-F1-MACRO: Label projection on Zebrafish (random split), Macro F1 score, Zebrafish (random split) (Open Problems label projection split)	Not reported	0.661	Not reported

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Exact evaluated configurations and original reported results
Tested configuration	Protocol and dataset	Finding	Evidence and details
Configuration: XGBoost (log scran)	Task: Open Problems label projection CENGEN-BATCH-ACCURACY: Label projection on CeNGEN (split by batch), Accuracy Dataset subset: CeNGEN (split by batch) (Open Problems label projection split)	0.83 Accuracy fraction · higher Uncertainty: Not yet extracted Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source XGBoost (log scran) on Open Problems label projection CENGEN-BATCH-ACCURACY: Label projection on CeNGEN (split by batch), Accuracy 100k FACS-isolated C. elegans neurons from 17 experiments sequenced on 10x Genomics. Split into train/test by experimental batch. Dimensions: 100955 cells, 22469 genes. 169 cell types (avg. 597±800 cells per cell type). Aggregation: Not reported openproblems-label primary benchmark evidence · results, dataset(cengen_batch), method(xgboost), paramset(log scran), metric(accuracy)
Configuration: XGBoost (log scran)	Task: Open Problems label projection CENGEN-BATCH-F1: Label projection on CeNGEN (split by batch), F1 score Dataset subset: CeNGEN (split by batch) (Open Problems label projection split)	0.842 Weighted F1 fraction · higher Uncertainty: Not yet extracted Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source XGBoost (log scran) on Open Problems label projection CENGEN-BATCH-F1: Label projection on CeNGEN (split by batch), F1 score 100k FACS-isolated C. elegans neurons from 17 experiments sequenced on 10x Genomics. Split into train/test by experimental batch. Dimensions: 100955 cells, 22469 genes. 169 cell types (avg. 597±800 cells per cell type). Aggregation: Not reported openproblems-label primary benchmark evidence · results, dataset(cengen_batch), method(xgboost), paramset(log scran), metric(f1)
Configuration: XGBoost (log scran)	Task: Open Problems label projection CENGEN-BATCH-F1-MACRO: Label projection on CeNGEN (split by batch), Macro F1 score Dataset subset: CeNGEN (split by batch) (Open Problems label projection split)	0.472 Macro F1 fraction · higher Uncertainty: Not yet extracted Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source XGBoost (log scran) on Open Problems label projection CENGEN-BATCH-F1-MACRO: Label projection on CeNGEN (split by batch), Macro F1 score 100k FACS-isolated C. elegans neurons from 17 experiments sequenced on 10x Genomics. Split into train/test by experimental batch. Dimensions: 100955 cells, 22469 genes. 169 cell types (avg. 597±800 cells per cell type). Aggregation: Not reported openproblems-label primary benchmark evidence · results, dataset(cengen_batch), method(xgboost), paramset(log scran), metric(f1_macro)
Configuration: XGBoost (log scran)	Task: Open Problems label projection CENGEN-RANDOM-ACCURACY: Label projection on CeNGEN (random split), Accuracy Dataset subset: CeNGEN (random split) (Open Problems label projection split)	0.821 Accuracy fraction · higher Uncertainty: Not yet extracted Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source XGBoost (log scran) on Open Problems label projection CENGEN-RANDOM-ACCURACY: Label projection on CeNGEN (random split), Accuracy 100k FACS-isolated C. elegans neurons from 17 experiments sequenced on 10x Genomics. Split into train/test randomly. Dimensions: 100955 cells, 22469 genes. 169 cell types avg. 597±800 cells per cell type). Aggregation: Not reported openproblems-label primary benchmark evidence · results, dataset(cengen_random), method(xgboost), paramset(log scran), metric(accuracy)
Configuration: XGBoost (log scran)	Task: Open Problems label projection CENGEN-RANDOM-F1: Label projection on CeNGEN (random split), F1 score Dataset subset: CeNGEN (random split) (Open Problems label projection split)	0.826 Weighted F1 fraction · higher Uncertainty: Not yet extracted Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source XGBoost (log scran) on Open Problems label projection CENGEN-RANDOM-F1: Label projection on CeNGEN (random split), F1 score 100k FACS-isolated C. elegans neurons from 17 experiments sequenced on 10x Genomics. Split into train/test randomly. Dimensions: 100955 cells, 22469 genes. 169 cell types avg. 597±800 cells per cell type). Aggregation: Not reported openproblems-label primary benchmark evidence · results, dataset(cengen_random), method(xgboost), paramset(log scran), metric(f1)
Configuration: XGBoost (log scran)	Task: Open Problems label projection CENGEN-RANDOM-F1-MACRO: Label projection on CeNGEN (random split), Macro F1 score Dataset subset: CeNGEN (random split) (Open Problems label projection split)	0.766 Macro F1 fraction · higher Uncertainty: Not yet extracted Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source XGBoost (log scran) on Open Problems label projection CENGEN-RANDOM-F1-MACRO: Label projection on CeNGEN (random split), Macro F1 score 100k FACS-isolated C. elegans neurons from 17 experiments sequenced on 10x Genomics. Split into train/test randomly. Dimensions: 100955 cells, 22469 genes. 169 cell types avg. 597±800 cells per cell type). Aggregation: Not reported openproblems-label primary benchmark evidence · results, dataset(cengen_random), method(xgboost), paramset(log scran), metric(f1_macro)
Configuration: XGBoost (log scran)	Task: Open Problems label projection PANCREAS-BATCH-ACCURACY: Label projection on Pancreas (by batch), Accuracy Dataset subset: Pancreas (by batch) (Open Problems label projection split)	0.934 Accuracy fraction · higher Uncertainty: Not yet extracted Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source XGBoost (log scran) on Open Problems label projection PANCREAS-BATCH-ACCURACY: Label projection on Pancreas (by batch), Accuracy Human pancreatic islet scRNA-seq data from 6 datasets across technologies (CEL-seq, CEL-seq2, Smart-seq2, inDrop, Fluidigm C1, and SMARTER-seq). Split into train/test by experimental batch. Dimensions: 16382 cells, 18771 genes. 14 cell types (avg. 1170±1703 cells per cell type). Aggregation: Not reported openproblems-label primary benchmark evidence · results, dataset(pancreas_batch), method(xgboost), paramset(log scran), metric(accuracy)
Configuration: XGBoost (log scran)	Task: Open Problems label projection PANCREAS-BATCH-F1: Label projection on Pancreas (by batch), F1 score Dataset subset: Pancreas (by batch) (Open Problems label projection split)	0.934 Weighted F1 fraction · higher Uncertainty: Not yet extracted Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source XGBoost (log scran) on Open Problems label projection PANCREAS-BATCH-F1: Label projection on Pancreas (by batch), F1 score Human pancreatic islet scRNA-seq data from 6 datasets across technologies (CEL-seq, CEL-seq2, Smart-seq2, inDrop, Fluidigm C1, and SMARTER-seq). Split into train/test by experimental batch. Dimensions: 16382 cells, 18771 genes. 14 cell types (avg. 1170±1703 cells per cell type). Aggregation: Not reported openproblems-label primary benchmark evidence · results, dataset(pancreas_batch), method(xgboost), paramset(log scran), metric(f1)
Configuration: XGBoost (log scran)	Task: Open Problems label projection PANCREAS-BATCH-F1-MACRO: Label projection on Pancreas (by batch), Macro F1 score Dataset subset: Pancreas (by batch) (Open Problems label projection split)	0.788 Macro F1 fraction · higher Uncertainty: Not yet extracted Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source XGBoost (log scran) on Open Problems label projection PANCREAS-BATCH-F1-MACRO: Label projection on Pancreas (by batch), Macro F1 score Human pancreatic islet scRNA-seq data from 6 datasets across technologies (CEL-seq, CEL-seq2, Smart-seq2, inDrop, Fluidigm C1, and SMARTER-seq). Split into train/test by experimental batch. Dimensions: 16382 cells, 18771 genes. 14 cell types (avg. 1170±1703 cells per cell type). Aggregation: Not reported openproblems-label primary benchmark evidence · results, dataset(pancreas_batch), method(xgboost), paramset(log scran), metric(f1_macro)
Configuration: XGBoost (log scran)	Task: Open Problems label projection PANCREAS-RANDOM-ACCURACY: Label projection on Pancreas (random split), Accuracy Dataset subset: Pancreas (random split) (Open Problems label projection split)	0.969 Accuracy fraction · higher Uncertainty: Not yet extracted Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source XGBoost (log scran) on Open Problems label projection PANCREAS-RANDOM-ACCURACY: Label projection on Pancreas (random split), Accuracy Human pancreatic islet scRNA-seq data from 6 datasets across technologies (CEL-seq, CEL-seq2, Smart-seq2, inDrop, Fluidigm C1, and SMARTER-seq). Split into train/test randomly. Dimensions: 16382 cells, 18771 genes. 14 cell types (avg. 1170±1703 cells per cell type). Aggregation: Not reported openproblems-label primary benchmark evidence · results, dataset(pancreas_random), method(xgboost), paramset(log scran), metric(accuracy)
Configuration: XGBoost (log scran)	Task: Open Problems label projection PANCREAS-RANDOM-F1: Label projection on Pancreas (random split), F1 score Dataset subset: Pancreas (random split) (Open Problems label projection split)	0.969 Weighted F1 fraction · higher Uncertainty: Not yet extracted Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source XGBoost (log scran) on Open Problems label projection PANCREAS-RANDOM-F1: Label projection on Pancreas (random split), F1 score Human pancreatic islet scRNA-seq data from 6 datasets across technologies (CEL-seq, CEL-seq2, Smart-seq2, inDrop, Fluidigm C1, and SMARTER-seq). Split into train/test randomly. Dimensions: 16382 cells, 18771 genes. 14 cell types (avg. 1170±1703 cells per cell type). Aggregation: Not reported openproblems-label primary benchmark evidence · results, dataset(pancreas_random), method(xgboost), paramset(log scran), metric(f1)
Configuration: XGBoost (log scran)	Task: Open Problems label projection PANCREAS-RANDOM-F1-MACRO: Label projection on Pancreas (random split), Macro F1 score Dataset subset: Pancreas (random split) (Open Problems label projection split)	0.919 Macro F1 fraction · higher Uncertainty: Not yet extracted Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source XGBoost (log scran) on Open Problems label projection PANCREAS-RANDOM-F1-MACRO: Label projection on Pancreas (random split), Macro F1 score Human pancreatic islet scRNA-seq data from 6 datasets across technologies (CEL-seq, CEL-seq2, Smart-seq2, inDrop, Fluidigm C1, and SMARTER-seq). Split into train/test randomly. Dimensions: 16382 cells, 18771 genes. 14 cell types (avg. 1170±1703 cells per cell type). Aggregation: Not reported openproblems-label primary benchmark evidence · results, dataset(pancreas_random), method(xgboost), paramset(log scran), metric(f1_macro)
Configuration: XGBoost (log scran)	Task: Open Problems label projection PANCREAS-RANDOM-LABEL-NOISE-ACCURACY: Label projection on Pancreas (random split with label noise), Accuracy Dataset subset: Pancreas (random split with label noise) (Open Problems label projection split)	0.953 Accuracy fraction · higher Uncertainty: Not yet extracted Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source XGBoost (log scran) on Open Problems label projection PANCREAS-RANDOM-LABEL-NOISE-ACCURACY: Label projection on Pancreas (random split with label noise), Accuracy Human pancreatic islet scRNA-seq data from 6 datasets across technologies (CEL-seq, CEL-seq2, Smart-seq2, inDrop, Fluidigm C1, and SMARTER-seq). Split into train/test randomly with 20% label noise. Dimensions: 16382 cells, 18771 genes. 14 cell types (avg. 1170±1703 cells per cell type). Aggregation: Not reported openproblems-label primary benchmark evidence · results, dataset(pancreas_random_label_noise), method(xgboost), paramset(log scran), metric(accuracy)
Configuration: XGBoost (log scran)	Task: Open Problems label projection PANCREAS-RANDOM-LABEL-NOISE-F1: Label projection on Pancreas (random split with label noise), F1 score Dataset subset: Pancreas (random split with label noise) (Open Problems label projection split)	0.953 Weighted F1 fraction · higher Uncertainty: Not yet extracted Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source XGBoost (log scran) on Open Problems label projection PANCREAS-RANDOM-LABEL-NOISE-F1: Label projection on Pancreas (random split with label noise), F1 score Human pancreatic islet scRNA-seq data from 6 datasets across technologies (CEL-seq, CEL-seq2, Smart-seq2, inDrop, Fluidigm C1, and SMARTER-seq). Split into train/test randomly with 20% label noise. Dimensions: 16382 cells, 18771 genes. 14 cell types (avg. 1170±1703 cells per cell type). Aggregation: Not reported openproblems-label primary benchmark evidence · results, dataset(pancreas_random_label_noise), method(xgboost), paramset(log scran), metric(f1)
Configuration: XGBoost (log scran)	Task: Open Problems label projection PANCREAS-RANDOM-LABEL-NOISE-F1-MACRO: Label projection on Pancreas (random split with label noise), Macro F1 score Dataset subset: Pancreas (random split with label noise) (Open Problems label projection split)	0.796 Macro F1 fraction · higher Uncertainty: Not yet extracted Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source XGBoost (log scran) on Open Problems label projection PANCREAS-RANDOM-LABEL-NOISE-F1-MACRO: Label projection on Pancreas (random split with label noise), Macro F1 score Human pancreatic islet scRNA-seq data from 6 datasets across technologies (CEL-seq, CEL-seq2, Smart-seq2, inDrop, Fluidigm C1, and SMARTER-seq). Split into train/test randomly with 20% label noise. Dimensions: 16382 cells, 18771 genes. 14 cell types (avg. 1170±1703 cells per cell type). Aggregation: Not reported openproblems-label primary benchmark evidence · results, dataset(pancreas_random_label_noise), method(xgboost), paramset(log scran), metric(f1_macro)
Configuration: XGBoost (log scran)	Task: Open Problems label projection TABULA-MURIS-SENIS-LUNG-RANDOM-ACCURACY: Label projection on Tabula Muris Senis Lung (random split), Accuracy Dataset subset: Tabula Muris Senis Lung (random split) (Open Problems label projection split)	0.865 Accuracy fraction · higher Uncertainty: Not yet extracted Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source XGBoost (log scran) on Open Problems label projection TABULA-MURIS-SENIS-LUNG-RANDOM-ACCURACY: Label projection on Tabula Muris Senis Lung (random split), Accuracy All lung cells from Tabula Muris Senis, a 500k cell-atlas from 18 organs and tissues across the mouse lifespan. Split into train/test randomly. Dimensions: 24540 cells, 17985 genes. 39 cell types (avg. 629±999 cells per cell type). Aggregation: Not reported openproblems-label primary benchmark evidence · results, dataset(tabula_muris_senis_lung_random), method(xgboost), paramset(log scran), metric(accuracy)
Configuration: XGBoost (log scran)	Task: Open Problems label projection TABULA-MURIS-SENIS-LUNG-RANDOM-F1: Label projection on Tabula Muris Senis Lung (random split), F1 score Dataset subset: Tabula Muris Senis Lung (random split) (Open Problems label projection split)	0.864 Weighted F1 fraction · higher Uncertainty: Not yet extracted Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source XGBoost (log scran) on Open Problems label projection TABULA-MURIS-SENIS-LUNG-RANDOM-F1: Label projection on Tabula Muris Senis Lung (random split), F1 score All lung cells from Tabula Muris Senis, a 500k cell-atlas from 18 organs and tissues across the mouse lifespan. Split into train/test randomly. Dimensions: 24540 cells, 17985 genes. 39 cell types (avg. 629±999 cells per cell type). Aggregation: Not reported openproblems-label primary benchmark evidence · results, dataset(tabula_muris_senis_lung_random), method(xgboost), paramset(log scran), metric(f1)
Configuration: XGBoost (log scran)	Task: Open Problems label projection TABULA-MURIS-SENIS-LUNG-RANDOM-F1-MACRO: Label projection on Tabula Muris Senis Lung (random split), Macro F1 score Dataset subset: Tabula Muris Senis Lung (random split) (Open Problems label projection split)	0.817 Macro F1 fraction · higher Uncertainty: Not yet extracted Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source XGBoost (log scran) on Open Problems label projection TABULA-MURIS-SENIS-LUNG-RANDOM-F1-MACRO: Label projection on Tabula Muris Senis Lung (random split), Macro F1 score All lung cells from Tabula Muris Senis, a 500k cell-atlas from 18 organs and tissues across the mouse lifespan. Split into train/test randomly. Dimensions: 24540 cells, 17985 genes. 39 cell types (avg. 629±999 cells per cell type). Aggregation: Not reported openproblems-label primary benchmark evidence · results, dataset(tabula_muris_senis_lung_random), method(xgboost), paramset(log scran), metric(f1_macro)
Configuration: XGBoost (log scran)	Task: Open Problems label projection ZEBRAFISH-LABS-ACCURACY: Label projection on Zebrafish (by laboratory), Accuracy Dataset subset: Zebrafish (by laboratory) (Open Problems label projection split)	0.236 Accuracy fraction · higher Uncertainty: Not yet extracted Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source XGBoost (log scran) on Open Problems label projection ZEBRAFISH-LABS-ACCURACY: Label projection on Zebrafish (by laboratory), Accuracy 90k cells from zebrafish embryos throughout the first day of development, with and without a knockout of chordin, an important developmental gene. Split into train/test by laboratory. Dimensions: 26022 cells, 25258 genes. 24 cell types (avg. 1084±1156 cells per cell type). Aggregation: Not reported openproblems-label primary benchmark evidence · results, dataset(zebrafish_labs), method(xgboost), paramset(log scran), metric(accuracy)
Configuration: XGBoost (log scran)	Task: Open Problems label projection ZEBRAFISH-LABS-F1: Label projection on Zebrafish (by laboratory), F1 score Dataset subset: Zebrafish (by laboratory) (Open Problems label projection split)	0.298 Weighted F1 fraction · higher Uncertainty: Not yet extracted Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source XGBoost (log scran) on Open Problems label projection ZEBRAFISH-LABS-F1: Label projection on Zebrafish (by laboratory), F1 score 90k cells from zebrafish embryos throughout the first day of development, with and without a knockout of chordin, an important developmental gene. Split into train/test by laboratory. Dimensions: 26022 cells, 25258 genes. 24 cell types (avg. 1084±1156 cells per cell type). Aggregation: Not reported openproblems-label primary benchmark evidence · results, dataset(zebrafish_labs), method(xgboost), paramset(log scran), metric(f1)
Configuration: XGBoost (log scran)	Task: Open Problems label projection ZEBRAFISH-LABS-F1-MACRO: Label projection on Zebrafish (by laboratory), Macro F1 score Dataset subset: Zebrafish (by laboratory) (Open Problems label projection split)	0.198 Macro F1 fraction · higher Uncertainty: Not yet extracted Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source XGBoost (log scran) on Open Problems label projection ZEBRAFISH-LABS-F1-MACRO: Label projection on Zebrafish (by laboratory), Macro F1 score 90k cells from zebrafish embryos throughout the first day of development, with and without a knockout of chordin, an important developmental gene. Split into train/test by laboratory. Dimensions: 26022 cells, 25258 genes. 24 cell types (avg. 1084±1156 cells per cell type). Aggregation: Not reported openproblems-label primary benchmark evidence · results, dataset(zebrafish_labs), method(xgboost), paramset(log scran), metric(f1_macro)
Configuration: XGBoost (log scran)	Task: Open Problems label projection ZEBRAFISH-RANDOM-ACCURACY: Label projection on Zebrafish (random split), Accuracy Dataset subset: Zebrafish (random split) (Open Problems label projection split)	0.788 Accuracy fraction · higher Uncertainty: Not yet extracted Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source XGBoost (log scran) on Open Problems label projection ZEBRAFISH-RANDOM-ACCURACY: Label projection on Zebrafish (random split), Accuracy 90k cells from zebrafish embryos throughout the first day of development, with and without a knockout of chordin, an important developmental gene. Split into train/test randomly. Dimensions: 26022 cells, 25258 genes. 24 cell types (avg. 1084±1156 cells per cell type). Aggregation: Not reported openproblems-label primary benchmark evidence · results, dataset(zebrafish_random), method(xgboost), paramset(log scran), metric(accuracy)
Configuration: XGBoost (log scran)	Task: Open Problems label projection ZEBRAFISH-RANDOM-F1: Label projection on Zebrafish (random split), F1 score Dataset subset: Zebrafish (random split) (Open Problems label projection split)	0.784 Weighted F1 fraction · higher Uncertainty: Not yet extracted Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source XGBoost (log scran) on Open Problems label projection ZEBRAFISH-RANDOM-F1: Label projection on Zebrafish (random split), F1 score 90k cells from zebrafish embryos throughout the first day of development, with and without a knockout of chordin, an important developmental gene. Split into train/test randomly. Dimensions: 26022 cells, 25258 genes. 24 cell types (avg. 1084±1156 cells per cell type). Aggregation: Not reported openproblems-label primary benchmark evidence · results, dataset(zebrafish_random), method(xgboost), paramset(log scran), metric(f1)
Configuration: XGBoost (log scran)	Task: Open Problems label projection ZEBRAFISH-RANDOM-F1-MACRO: Label projection on Zebrafish (random split), Macro F1 score Dataset subset: Zebrafish (random split) (Open Problems label projection split)	0.661 Macro F1 fraction · higher Uncertainty: Not yet extracted Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source XGBoost (log scran) on Open Problems label projection ZEBRAFISH-RANDOM-F1-MACRO: Label projection on Zebrafish (random split), Macro F1 score 90k cells from zebrafish embryos throughout the first day of development, with and without a knockout of chordin, an important developmental gene. Split into train/test randomly. Dimensions: 26022 cells, 25258 genes. 24 cell types (avg. 1084±1156 cells per cell type). Aggregation: Not reported openproblems-label primary benchmark evidence · results, dataset(zebrafish_random), method(xgboost), paramset(log scran), metric(f1_macro)

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