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Question about predictor output: Score and PredictedLabel columns #376
Description
Current two tutorials in the docs use different columns to get a predicted value out of the pipeline into an instance of the user-defined prediction type:
- Regression taxi fare tutorial uses the Score column
- Binary classification sentiment analysis tutorial uses the PredictedLabel column
How does one know which column to use to populate instances of the prediction type? Especially given that, in case of the (binary) classification solution, the Score column is also available (I guess, then it contains the probabilities of being in a certain class).
As for the trainer inputs, rules are more or less clear:
- Use the Label column for labels (or specify another column name through the
LabelColumnproperty) - Use the Features column for features (or specify another column name through the
FeatureColumnproperty)
Can the setup of the predictor output be done in similar way:
- Use the column with the same name across all the predictors for the predictor output. I guess that might require to extend regression
IDataViewwith the PredictedLabel column that would be a copy of the Score column. - Be able to setup the name of the output column. (That seems the PredictedLabelColumnOriginalValueConverter can be used for that; or I'm wrong and that class is intended for use in tandem with the Dictionarizer?)
By the way, the mere explanation of the Score and PredictedLabel columns here would be appreciated as well. Then, at least, I'll update the docs to make story clearer.
Activity
zeahmed commentedon Jun 22, 2018
The purpose of the output columns in scored IDataView is according to the learning task. e.g. if task is
Regression
Binary Classification
Multi-class Classification
Clustering
pkulikov commentedon Jun 22, 2018
@zeahmed thank you very much for the answer. I'll keep that information in mind while updating the docs.
Are there any plans to unify meaning of the columns across various learning tasks or introduce any other changes in this area of ML.NET. Or that part is more or less stable?
arafattehsin commentedon Jul 14, 2018
Thank you @zeahmed for the detailed answer.
Lanayx commentedon Jul 22, 2018
@zeahmed I've been trying to use Probability column in EnsembleBinaryClassifier, but it fails with
'Column 'Probability' not found in the data view'. Is it a bug or by design?Ivanidzo4ka commentedon Oct 18, 2018
DRI RESPONSE: I can't find this information in our docs, and it definitely should be there. Wherefore marking this as documentation and up-for-grabs.
@Lanayx I'll suggest you to create separate issue, and provide us additional information (code snippet at least).
pkulikov commentedon Oct 19, 2018
@JRAlexander @Ivanidzo4ka is that something that should be at https://docs.microsoft.com/en-us/dotnet/machine-learning/ ?
dilmanous commentedon Dec 29, 2018
Is there a way to get class label names along with the probabilities that we get from Multi-class classification? ryGetScoreLabelNames is part of legacy code in 0.8
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shmoradims commentedon May 20, 2019
I believe this issue is already addressed as part of 1.0 API reference documentation. Now all trainers have a sub-section in their remarks called input/output columns, where the types and definition of the input/output columns are clearly explained. E.g.: https://docs.microsoft.com/en-us/dotnet/api/microsoft.ml.trainers.averagedperceptrontrainer?view=ml-dotnet#input-and-output-columns
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maheshlokhande commentedon Jan 6, 2022
For Multi-class classification with NavieBayes score column contains negatives values for each class. How to convert it to probability?
HolyOne commentedon Mar 6, 2022
I am also confused with required ColumnName attributes, diffrent for every ML algortihm.
Why not force an interface like IRegressionOutput on the output ( or IBinaryClassificationOutpu for binary classification case) type so we can know which properties to implement when designing Input and output?