[
https://issues.apache.org/jira/browse/SOLR-13903?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Ahmed Adel updated SOLR-13903:
------------------------------
Description:
Using features and train stream sources generate a model with TP, TN, FP, FN
fields. For some reason, the summation of the values of these fields is
sometimes less than the training set size.
How to regenerate:
1. Create two collections: cellphones and cellphones-model
2. Indexing the attached dataset into cellphones
3. Run the following expression:
{{commit(cellphones-model,update(cellphones-model,batchSize=500,}}
{{ train(cellphones,}}
{{ features(cellphones, q="*:*", featureSet="featureSet",}}
{{ field="title_t",}}
{{ outcome="brand_i", numTerms=25),}}
{{ q="*:*",}}
{{ name="cellphones-classification-model",}}
{{ field="title_t",}}
{{ outcome="brand_i",}}
{{ maxIterations=100)))}}
4. Run the following query to retrieve confusion matrix:
{{search
q=*:*&collection=cellphones-model&fl=name_s,trueNegative_i,truePositive_i,falseNegative_i,falsePositive_i,iteration_i&sort=iteration_i%20desc&rows=100}}
The summation of the metrics TP, TN, FP, FN is always less than the training
set size by one in this instance for all iterations.
was:
Using features and train stream sources generate a model with TP, TN, FP, FN
fields. For some reason, the summation of the values of these fields is
sometimes less than the training set size.
How to regenerate:
# Create two collections: cellphones and cellphones-model
# Indexing the attached dataset into cellphones
# Run the following expression:
{{commit(cellphones-model,update(cellphones-model,batchSize=500,
train(cellphones,
features(cellphones, q="*:*", featureSet="featureSet",
field="title_t",
outcome="brand_i", numTerms=25),
q="*:*",
name="cellphones-classification-model",
field="title_t",
outcome="brand_i",
maxIterations=100)))
}}
# Run the following query to retrieve confusion matrix:
{{search
q=*:*&collection=cellphones-model&fl=name_s,trueNegative_i,truePositive_i,falseNegative_i,falsePositive_i,iteration_i&sort=iteration_i%20desc&rows=100}}
The summation of the metrics TP, TN, FP, FN is always less than the training
set size by one in this instance for all iterations.
> Classification Model Confusion Matrix Discrepancy
> -------------------------------------------------
>
> Key: SOLR-13903
> URL: https://issues.apache.org/jira/browse/SOLR-13903
> Project: Solr
> Issue Type: Bug
> Security Level: Public(Default Security Level. Issues are Public)
> Components: streaming expressions
> Affects Versions: 8.2
> Reporter: Ahmed Adel
> Priority: Major
> Labels: classification
> Attachments: cellphones.csv
>
>
> Using features and train stream sources generate a model with TP, TN, FP, FN
> fields. For some reason, the summation of the values of these fields is
> sometimes less than the training set size.
> How to regenerate:
> 1. Create two collections: cellphones and cellphones-model
> 2. Indexing the attached dataset into cellphones
> 3. Run the following expression:
> {{commit(cellphones-model,update(cellphones-model,batchSize=500,}}
> {{ train(cellphones,}}
> {{ features(cellphones, q="*:*", featureSet="featureSet",}}
> {{ field="title_t",}}
> {{ outcome="brand_i", numTerms=25),}}
> {{ q="*:*",}}
> {{ name="cellphones-classification-model",}}
> {{ field="title_t",}}
> {{ outcome="brand_i",}}
> {{ maxIterations=100)))}}
> 4. Run the following query to retrieve confusion matrix:
> {{search
> q=*:*&collection=cellphones-model&fl=name_s,trueNegative_i,truePositive_i,falseNegative_i,falsePositive_i,iteration_i&sort=iteration_i%20desc&rows=100}}
> The summation of the metrics TP, TN, FP, FN is always less than the training
> set size by one in this instance for all iterations.
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