Confusionmatrix for linear regression
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Hi,
I'm using the code below to train a logistic regression classifier. I'd like to plot a confusion matrix but even though i waited 30 minutes, it doesn't show the matrix. I just run the code and it goes on debugging but never shows the result. The predicted and response matrix consits of 5000x1 matrix that has either 0 or 1. I don't think plotting a confusion matrix for this data would take that long. Can anybody help me about the problem ?
strokedata=importstroke("healthcare-dataset-stroke-data")
inputTable=strokedata;
predictorNames={'gender','age','hypertension','heart_disease','work_type','Residence_type','avg_glucose_level','bmi','smoking_status'}
predictors = inputTable(:, predictorNames)
response=inputTable.stroke
isCategoricalPredictor=[true,false,false,false,true,true,true,false,false,true]
successClass = double(1);
failureClass = double(0);
numSuccess = sum(response == successClass);
numFailure = sum(response == failureClass);
if numSuccess > numFailure
missingClass = successClass
else
missingClass = failureClass
end
successFailureAndMissingClasses = [successClass; failureClass; missingClass];
isMissing = isnan(response)
zeroOneResponse = double(ismember(response, successClass))
zeroOneResponse(isMissing) = NaN
% Prepare input arguments to fitglm.
concatenatedPredictorsAndResponse = [predictors, table(zeroOneResponse)]
GeneralizedLinearModel = fitglm(...
concatenatedPredictorsAndResponse, ...
'Distribution', 'binomial','link','logit')
yPredicted=predict(GeneralizedLinearModel,inputTable) > 0.47
plotconfusion(response,yPredicted)
6 Comments
the cyclist
on 22 Apr 2021
I don't have the Deep Learning Toolbox. But
confusionchart(logical(response),yPredicted) % requires Stats & Machine Learning Toolbox
returned the chart in under a second. So, I think your instinct is correct. I'm not sure what's going on in your code.
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