Logistic regression (log CP10k)
Logistic Regression estimates parameters of a logistic function for multivariate classification tasks. Here, we use 100-dimensional whitened PCA coordinates as independent variables, and the model minimises the cross entropy loss over all cell type classes.
Overview
Logistic Regression estimates parameters of a logistic function for multivariate classification tasks. Here, we use 100-dimensional whitened PCA coordinates as independent variables, and the model minimises the cross entropy loss over all cell type classes.
Consult the linked sources for architecture or protocol details. Missing evidence is not evidence of a missing capability.
Evaluations and results
Release 2026-09-17-134cd1815de8 · 24 evaluations · 24 metric rows. Different protocols are not a single leaderboard. Where several source tables report the same metric, the published comparisons above offer a pooled view that names what it does not hold constant.
| Metric and finding | Coverage and uncertainty | Evidence |
|---|---|---|
| Logistic regression (log CP10k) on Open Problems label projection CENGEN-BATCH-ACCURACY: Label projection on CeNGEN (split by batch), Accuracy Configuration: Logistic regression (log CP10k)Task: Open Problems label projection CENGEN-BATCH-ACCURACY: Label projection on CeNGEN (split by batch), AccuracyDataset subset: CeNGEN (split by batch) (Open Problems label projection split) 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). Author-reported evaluation · Evaluation metadata: source checked | ||
| 0.8783702816057519 accuracy Unit: fraction · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedopenproblems-label primary benchmark evidence · results, dataset(cengen_batch), method(logistic_regression), paramset(log CP10k), metric(accuracy) Source checking is not independent reproduction. |
| Logistic regression (log CP10k) on Open Problems label projection CENGEN-BATCH-F1: Label projection on CeNGEN (split by batch), F1 score Configuration: Logistic regression (log CP10k)Task: Open Problems label projection CENGEN-BATCH-F1: Label projection on CeNGEN (split by batch), F1 scoreDataset subset: CeNGEN (split by batch) (Open Problems label projection split) 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). Author-reported evaluation · Evaluation metadata: source checked | ||
| 0.875140390576727 f1 Unit: fraction · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedopenproblems-label primary benchmark evidence · results, dataset(cengen_batch), method(logistic_regression), paramset(log CP10k), metric(f1) Source checking is not independent reproduction. |
| Logistic regression (log CP10k) on Open Problems label projection CENGEN-BATCH-F1-MACRO: Label projection on CeNGEN (split by batch), Macro F1 score Configuration: Logistic regression (log CP10k)Task: Open Problems label projection CENGEN-BATCH-F1-MACRO: Label projection on CeNGEN (split by batch), Macro F1 scoreDataset subset: CeNGEN (split by batch) (Open Problems label projection split) 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). Author-reported evaluation · Evaluation metadata: source checked | ||
| 0.5108683585871248 f1-macro Unit: fraction · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedopenproblems-label primary benchmark evidence · results, dataset(cengen_batch), method(logistic_regression), paramset(log CP10k), metric(f1_macro) Source checking is not independent reproduction. |
| Logistic regression (log CP10k) on Open Problems label projection CENGEN-RANDOM-ACCURACY: Label projection on CeNGEN (random split), Accuracy Configuration: Logistic regression (log CP10k)Task: Open Problems label projection CENGEN-RANDOM-ACCURACY: Label projection on CeNGEN (random split), AccuracyDataset subset: CeNGEN (random split) (Open Problems label projection split) 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). Author-reported evaluation · Evaluation metadata: source checked | ||
| 0.8904041901373654 accuracy Unit: fraction · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedopenproblems-label primary benchmark evidence · results, dataset(cengen_random), method(logistic_regression), paramset(log CP10k), metric(accuracy) Source checking is not independent reproduction. |
| Logistic regression (log CP10k) on Open Problems label projection CENGEN-RANDOM-F1: Label projection on CeNGEN (random split), F1 score Configuration: Logistic regression (log CP10k)Task: Open Problems label projection CENGEN-RANDOM-F1: Label projection on CeNGEN (random split), F1 scoreDataset subset: CeNGEN (random split) (Open Problems label projection split) 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). Author-reported evaluation · Evaluation metadata: source checked | ||
| 0.8907696437309108 f1 Unit: fraction · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedopenproblems-label primary benchmark evidence · results, dataset(cengen_random), method(logistic_regression), paramset(log CP10k), metric(f1) Source checking is not independent reproduction. |
| Logistic regression (log CP10k) on Open Problems label projection CENGEN-RANDOM-F1-MACRO: Label projection on CeNGEN (random split), Macro F1 score Configuration: Logistic regression (log CP10k)Task: Open Problems label projection CENGEN-RANDOM-F1-MACRO: Label projection on CeNGEN (random split), Macro F1 scoreDataset subset: CeNGEN (random split) (Open Problems label projection split) 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). Author-reported evaluation · Evaluation metadata: source checked | ||
| 0.8282477715392297 f1-macro Unit: fraction · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedopenproblems-label primary benchmark evidence · results, dataset(cengen_random), method(logistic_regression), paramset(log CP10k), metric(f1_macro) Source checking is not independent reproduction. |
| Logistic regression (log CP10k) on Open Problems label projection PANCREAS-BATCH-ACCURACY: Label projection on Pancreas (by batch), Accuracy Configuration: Logistic regression (log CP10k)Task: Open Problems label projection PANCREAS-BATCH-ACCURACY: Label projection on Pancreas (by batch), AccuracyDataset subset: Pancreas (by batch) (Open Problems label projection split) 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). Author-reported evaluation · Evaluation metadata: source checked | ||
| 0.9634839058696241 accuracy Unit: fraction · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedopenproblems-label primary benchmark evidence · results, dataset(pancreas_batch), method(logistic_regression), paramset(log CP10k), metric(accuracy) Source checking is not independent reproduction. |
| Logistic regression (log CP10k) on Open Problems label projection PANCREAS-BATCH-F1: Label projection on Pancreas (by batch), F1 score Configuration: Logistic regression (log CP10k)Task: Open Problems label projection PANCREAS-BATCH-F1: Label projection on Pancreas (by batch), F1 scoreDataset subset: Pancreas (by batch) (Open Problems label projection split) 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). Author-reported evaluation · Evaluation metadata: source checked | ||
| 0.9639430099980332 f1 Unit: fraction · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedopenproblems-label primary benchmark evidence · results, dataset(pancreas_batch), method(logistic_regression), paramset(log CP10k), metric(f1) Source checking is not independent reproduction. |
| Logistic regression (log CP10k) on Open Problems label projection PANCREAS-BATCH-F1-MACRO: Label projection on Pancreas (by batch), Macro F1 score Configuration: Logistic regression (log CP10k)Task: Open Problems label projection PANCREAS-BATCH-F1-MACRO: Label projection on Pancreas (by batch), Macro F1 scoreDataset subset: Pancreas (by batch) (Open Problems label projection split) 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). Author-reported evaluation · Evaluation metadata: source checked | ||
| 0.9390204193334389 f1-macro Unit: fraction · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedopenproblems-label primary benchmark evidence · results, dataset(pancreas_batch), method(logistic_regression), paramset(log CP10k), metric(f1_macro) Source checking is not independent reproduction. |
| Logistic regression (log CP10k) on Open Problems label projection PANCREAS-RANDOM-ACCURACY: Label projection on Pancreas (random split), Accuracy Configuration: Logistic regression (log CP10k)Task: Open Problems label projection PANCREAS-RANDOM-ACCURACY: Label projection on Pancreas (random split), AccuracyDataset subset: Pancreas (random split) (Open Problems label projection split) 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). Author-reported evaluation · Evaluation metadata: source checked | ||
| 0.9878419452887538 accuracy Unit: fraction · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedopenproblems-label primary benchmark evidence · results, dataset(pancreas_random), method(logistic_regression), paramset(log CP10k), metric(accuracy) Source checking is not independent reproduction. |
| Logistic regression (log CP10k) on Open Problems label projection PANCREAS-RANDOM-F1: Label projection on Pancreas (random split), F1 score Configuration: Logistic regression (log CP10k)Task: Open Problems label projection PANCREAS-RANDOM-F1: Label projection on Pancreas (random split), F1 scoreDataset subset: Pancreas (random split) (Open Problems label projection split) 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). Author-reported evaluation · Evaluation metadata: source checked | ||
| 0.9877826521340638 f1 Unit: fraction · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedopenproblems-label primary benchmark evidence · results, dataset(pancreas_random), method(logistic_regression), paramset(log CP10k), metric(f1) Source checking is not independent reproduction. |
| Logistic regression (log CP10k) on Open Problems label projection PANCREAS-RANDOM-F1-MACRO: Label projection on Pancreas (random split), Macro F1 score Configuration: Logistic regression (log CP10k)Task: Open Problems label projection PANCREAS-RANDOM-F1-MACRO: Label projection on Pancreas (random split), Macro F1 scoreDataset subset: Pancreas (random split) (Open Problems label projection split) 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). Author-reported evaluation · Evaluation metadata: source checked | ||
| 0.9723208693493605 f1-macro Unit: fraction · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedopenproblems-label primary benchmark evidence · results, dataset(pancreas_random), method(logistic_regression), paramset(log CP10k), metric(f1_macro) Source checking is not independent reproduction. |
| Logistic regression (log CP10k) on Open Problems label projection PANCREAS-RANDOM-LABEL-NOISE-ACCURACY: Label projection on Pancreas (random split with label noise), Accuracy Configuration: Logistic regression (log CP10k)Task: Open Problems label projection PANCREAS-RANDOM-LABEL-NOISE-ACCURACY: Label projection on Pancreas (random split with label noise), AccuracyDataset subset: Pancreas (random split with label noise) (Open Problems label projection split) 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). Author-reported evaluation · Evaluation metadata: source checked | ||
| 0.9796610169491525 accuracy Unit: fraction · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedopenproblems-label primary benchmark evidence · results, dataset(pancreas_random_label_noise), method(logistic_regression), paramset(log CP10k), metric(accuracy) Source checking is not independent reproduction. |
| Logistic regression (log CP10k) on Open Problems label projection PANCREAS-RANDOM-LABEL-NOISE-F1: Label projection on Pancreas (random split with label noise), F1 score Configuration: Logistic regression (log CP10k)Task: Open Problems label projection PANCREAS-RANDOM-LABEL-NOISE-F1: Label projection on Pancreas (random split with label noise), F1 scoreDataset subset: Pancreas (random split with label noise) (Open Problems label projection split) 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). Author-reported evaluation · Evaluation metadata: source checked | ||
| 0.9791824244686764 f1 Unit: fraction · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedopenproblems-label primary benchmark evidence · results, dataset(pancreas_random_label_noise), method(logistic_regression), paramset(log CP10k), metric(f1) Source checking is not independent reproduction. |
| Logistic regression (log CP10k) on Open Problems label projection PANCREAS-RANDOM-LABEL-NOISE-F1-MACRO: Label projection on Pancreas (random split with label noise), Macro F1 score Configuration: Logistic regression (log CP10k)Task: Open Problems label projection PANCREAS-RANDOM-LABEL-NOISE-F1-MACRO: Label projection on Pancreas (random split with label noise), Macro F1 scoreDataset subset: Pancreas (random split with label noise) (Open Problems label projection split) 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). Author-reported evaluation · Evaluation metadata: source checked | ||
| 0.8964053737821305 f1-macro Unit: fraction · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedopenproblems-label primary benchmark evidence · results, dataset(pancreas_random_label_noise), method(logistic_regression), paramset(log CP10k), metric(f1_macro) Source checking is not independent reproduction. |
| Logistic regression (log CP10k) on Open Problems label projection TABULA-MURIS-SENIS-LUNG-RANDOM-ACCURACY: Label projection on Tabula Muris Senis Lung (random split), Accuracy Configuration: Logistic regression (log CP10k)Task: Open Problems label projection TABULA-MURIS-SENIS-LUNG-RANDOM-ACCURACY: Label projection on Tabula Muris Senis Lung (random split), AccuracyDataset subset: Tabula Muris Senis Lung (random split) (Open Problems label projection split) 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). Author-reported evaluation · Evaluation metadata: source checked | ||
| 0.925777331995988 accuracy Unit: fraction · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedopenproblems-label primary benchmark evidence · results, dataset(tabula_muris_senis_lung_random), method(logistic_regression), paramset(log CP10k), metric(accuracy) Source checking is not independent reproduction. |
| Logistic regression (log CP10k) on Open Problems label projection TABULA-MURIS-SENIS-LUNG-RANDOM-F1: Label projection on Tabula Muris Senis Lung (random split), F1 score Configuration: Logistic regression (log CP10k)Task: Open Problems label projection TABULA-MURIS-SENIS-LUNG-RANDOM-F1: Label projection on Tabula Muris Senis Lung (random split), F1 scoreDataset subset: Tabula Muris Senis Lung (random split) (Open Problems label projection split) 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). Author-reported evaluation · Evaluation metadata: source checked | ||
| 0.9252414029903501 f1 Unit: fraction · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedopenproblems-label primary benchmark evidence · results, dataset(tabula_muris_senis_lung_random), method(logistic_regression), paramset(log CP10k), metric(f1) Source checking is not independent reproduction. |
| Logistic regression (log CP10k) on Open Problems label projection TABULA-MURIS-SENIS-LUNG-RANDOM-F1-MACRO: Label projection on Tabula Muris Senis Lung (random split), Macro F1 score Configuration: Logistic regression (log CP10k)Task: Open Problems label projection TABULA-MURIS-SENIS-LUNG-RANDOM-F1-MACRO: Label projection on Tabula Muris Senis Lung (random split), Macro F1 scoreDataset subset: Tabula Muris Senis Lung (random split) (Open Problems label projection split) 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). Author-reported evaluation · Evaluation metadata: source checked | ||
| 0.9131036540313611 f1-macro Unit: fraction · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedopenproblems-label primary benchmark evidence · results, dataset(tabula_muris_senis_lung_random), method(logistic_regression), paramset(log CP10k), metric(f1_macro) Source checking is not independent reproduction. |
| Logistic regression (log CP10k) on Open Problems label projection ZEBRAFISH-LABS-ACCURACY: Label projection on Zebrafish (by laboratory), Accuracy Configuration: Logistic regression (log CP10k)Task: Open Problems label projection ZEBRAFISH-LABS-ACCURACY: Label projection on Zebrafish (by laboratory), AccuracyDataset subset: Zebrafish (by laboratory) (Open Problems label projection split) 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). Author-reported evaluation · Evaluation metadata: source checked | ||
| 0.24802127935642923 accuracy Unit: fraction · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedopenproblems-label primary benchmark evidence · results, dataset(zebrafish_labs), method(logistic_regression), paramset(log CP10k), metric(accuracy) Source checking is not independent reproduction. |
| Logistic regression (log CP10k) on Open Problems label projection ZEBRAFISH-LABS-F1: Label projection on Zebrafish (by laboratory), F1 score Configuration: Logistic regression (log CP10k)Task: Open Problems label projection ZEBRAFISH-LABS-F1: Label projection on Zebrafish (by laboratory), F1 scoreDataset subset: Zebrafish (by laboratory) (Open Problems label projection split) 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). Author-reported evaluation · Evaluation metadata: source checked | ||
| 0.2875142098321384 f1 Unit: fraction · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedopenproblems-label primary benchmark evidence · results, dataset(zebrafish_labs), method(logistic_regression), paramset(log CP10k), metric(f1) Source checking is not independent reproduction. |
| Logistic regression (log CP10k) on Open Problems label projection ZEBRAFISH-LABS-F1-MACRO: Label projection on Zebrafish (by laboratory), Macro F1 score Configuration: Logistic regression (log CP10k)Task: Open Problems label projection ZEBRAFISH-LABS-F1-MACRO: Label projection on Zebrafish (by laboratory), Macro F1 scoreDataset subset: Zebrafish (by laboratory) (Open Problems label projection split) 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). Author-reported evaluation · Evaluation metadata: source checked | ||
| 0.2327128251184122 f1-macro Unit: fraction · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedopenproblems-label primary benchmark evidence · results, dataset(zebrafish_labs), method(logistic_regression), paramset(log CP10k), metric(f1_macro) Source checking is not independent reproduction. |
| Logistic regression (log CP10k) on Open Problems label projection ZEBRAFISH-RANDOM-ACCURACY: Label projection on Zebrafish (random split), Accuracy Configuration: Logistic regression (log CP10k)Task: Open Problems label projection ZEBRAFISH-RANDOM-ACCURACY: Label projection on Zebrafish (random split), AccuracyDataset subset: Zebrafish (random split) (Open Problems label projection split) 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). Author-reported evaluation · Evaluation metadata: source checked | ||
| 0.8426140757927301 accuracy Unit: fraction · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedopenproblems-label primary benchmark evidence · results, dataset(zebrafish_random), method(logistic_regression), paramset(log CP10k), metric(accuracy) Source checking is not independent reproduction. |
| Logistic regression (log CP10k) on Open Problems label projection ZEBRAFISH-RANDOM-F1: Label projection on Zebrafish (random split), F1 score Configuration: Logistic regression (log CP10k)Task: Open Problems label projection ZEBRAFISH-RANDOM-F1: Label projection on Zebrafish (random split), F1 scoreDataset subset: Zebrafish (random split) (Open Problems label projection split) 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). Author-reported evaluation · Evaluation metadata: source checked | ||
| 0.8410077225303916 f1 Unit: fraction · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedopenproblems-label primary benchmark evidence · results, dataset(zebrafish_random), method(logistic_regression), paramset(log CP10k), metric(f1) Source checking is not independent reproduction. |
| Logistic regression (log CP10k) on Open Problems label projection ZEBRAFISH-RANDOM-F1-MACRO: Label projection on Zebrafish (random split), Macro F1 score Configuration: Logistic regression (log CP10k)Task: Open Problems label projection ZEBRAFISH-RANDOM-F1-MACRO: Label projection on Zebrafish (random split), Macro F1 scoreDataset subset: Zebrafish (random split) (Open Problems label projection split) 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). Author-reported evaluation · Evaluation metadata: source checked | ||
| 0.7110845534586984 f1-macro Unit: fraction · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedopenproblems-label primary benchmark evidence · results, dataset(zebrafish_random), method(logistic_regression), paramset(log CP10k), metric(f1_macro) Source checking is not independent reproduction. |
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Release 2026-09-17-134cd1815de8 · Record review: source checked
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Stable ID: open-problems-method-logistic-regression-log-cp10k
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Related records
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- model: Logistic regression (log CP10k) on Open Problems label projection CENGEN-BATCH-F1: Label projection on CeNGEN (split by batch), F1 score
- model: Logistic regression (log CP10k) on Open Problems label projection CENGEN-BATCH-F1-MACRO: Label projection on CeNGEN (split by batch), Macro F1 score
- model: Logistic regression (log CP10k) on Open Problems label projection CENGEN-RANDOM-ACCURACY: Label projection on CeNGEN (random split), Accuracy
- model: Logistic regression (log CP10k) on Open Problems label projection CENGEN-RANDOM-F1: Label projection on CeNGEN (random split), F1 score
- model: Logistic regression (log CP10k) on Open Problems label projection CENGEN-RANDOM-F1-MACRO: Label projection on CeNGEN (random split), Macro F1 score
- model: Logistic regression (log CP10k) on Open Problems label projection PANCREAS-BATCH-ACCURACY: Label projection on Pancreas (by batch), Accuracy
- model: Logistic regression (log CP10k) on Open Problems label projection PANCREAS-BATCH-F1: Label projection on Pancreas (by batch), F1 score
- model: Logistic regression (log CP10k) on Open Problems label projection PANCREAS-BATCH-F1-MACRO: Label projection on Pancreas (by batch), Macro F1 score
- model: Logistic regression (log CP10k) on Open Problems label projection PANCREAS-RANDOM-ACCURACY: Label projection on Pancreas (random split), Accuracy
- model: Logistic regression (log CP10k) on Open Problems label projection PANCREAS-RANDOM-F1: Label projection on Pancreas (random split), F1 score
- model: Logistic regression (log CP10k) on Open Problems label projection PANCREAS-RANDOM-F1-MACRO: Label projection on Pancreas (random split), Macro F1 score
- model: Logistic regression (log CP10k) on Open Problems label projection PANCREAS-RANDOM-LABEL-NOISE-ACCURACY: Label projection on Pancreas (random split with label noise), Accuracy
- model: Logistic regression (log CP10k) on Open Problems label projection PANCREAS-RANDOM-LABEL-NOISE-F1: Label projection on Pancreas (random split with label noise), F1 score
- model: Logistic regression (log CP10k) on Open Problems label projection PANCREAS-RANDOM-LABEL-NOISE-F1-MACRO: Label projection on Pancreas (random split with label noise), Macro F1 score
- model: Logistic regression (log CP10k) on Open Problems label projection TABULA-MURIS-SENIS-LUNG-RANDOM-ACCURACY: Label projection on Tabula Muris Senis Lung (random split), Accuracy
- model: Logistic regression (log CP10k) on Open Problems label projection TABULA-MURIS-SENIS-LUNG-RANDOM-F1: Label projection on Tabula Muris Senis Lung (random split), F1 score
- model: Logistic regression (log CP10k) on Open Problems label projection TABULA-MURIS-SENIS-LUNG-RANDOM-F1-MACRO: Label projection on Tabula Muris Senis Lung (random split), Macro F1 score
- model: Logistic regression (log CP10k) on Open Problems label projection ZEBRAFISH-LABS-ACCURACY: Label projection on Zebrafish (by laboratory), Accuracy
- model: Logistic regression (log CP10k) on Open Problems label projection ZEBRAFISH-LABS-F1: Label projection on Zebrafish (by laboratory), F1 score
- model: Logistic regression (log CP10k) on Open Problems label projection ZEBRAFISH-LABS-F1-MACRO: Label projection on Zebrafish (by laboratory), Macro F1 score
- model: Logistic regression (log CP10k) on Open Problems label projection ZEBRAFISH-RANDOM-ACCURACY: Label projection on Zebrafish (random split), Accuracy
- model: Logistic regression (log CP10k) on Open Problems label projection ZEBRAFISH-RANDOM-F1: Label projection on Zebrafish (random split), F1 score
- model: Logistic regression (log CP10k) on Open Problems label projection ZEBRAFISH-RANDOM-F1-MACRO: Label projection on Zebrafish (random split), Macro F1 score