Comments (2)
@lxhuang7, you need to write your own code to assess the performance of the model, such as 1) testing it on an independent new dataset (with ground-truth cell types available) to obtain the precision, recall, F1, AUROC, etc, or 2) train-test splitting your training dataset to obtain these metrics (and to find the best parameter combo).
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@ChuanXu1 , Should I train a model using all my annotated data to get the final model? or use cv method to train model on training data, then validate on validating data, then test the model on testing data, then pick up a model with the best performance as the final model?
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Related Issues (20)
- " have completed preprocessing and cell clustering. " HOT 2
- codes for harmonizing the cell labels HOT 2
- the question about conf_score HOT 3
- CellTypist_Immune_Reference_v2 question HOT 2
- Immune_All_Low - model training question HOT 4
- Cluster-level label prediction HOT 1
- Running celltypist with concatenated dataset HOT 3
- Running the CellTypist training function celltypist.train on a subset of genes HOT 1
- ValueError in celltypist.annotate HOT 3
- is it available to generate model.pkl file from a marker gene list HOT 1
- Feature: Support Rapids-singlecell HOT 1
- Can not detect a neighborhood graph, will construct one before the over-clustering HOT 1
- multiple models HOT 3
- Question about over_clustering and conf_score? HOT 1
- Conflict with variable genes and model training HOT 1
- www.celltypist.org is down, cannot download models HOT 3
- Can not download the model HOT 2
- Very low confidence score even though labels are correct HOT 2
- unable to download models HOT 3
- Feature Request: Store freqs in annotation when majority_vote set to True HOT 2
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