How to compare two sets of class frequency data?
$begingroup$
I am working with a machine learning approach that counts 2 classes of objects in images: people and cars. I have a predicted dataset, which is the predicted output from the machine learning approach and a "true" dataset which is the result of a human going through each image and counting people and cars. The following is a sample of what the datasets look like:
Image 1
Class Predicted TRUE
People 6 6
Cars 2 1
Image 2
Class Predicted TRUE
People 0 0
Cars 0 0
... and so on ...
Image 5000
Class Predicted TRUE
People 2 4
Cars 1 1
I am assuming that I cannot use a confusion matrix to assess the accuracy because I am dealing with class frequency data for each image. What approach can I take to assess the accuracy of the predicted vs true datasets?
machine-learning accuracy
asked 4 mins ago
BorealisBorealis
172212
$endgroup$
add a comment |
$begingroup$
I am working with a machine learning approach that counts 2 classes of objects in images: people and cars. I have a predicted dataset, which is the predicted output from the machine learning approach and a "true" dataset which is the result of a human going through each image and counting people and cars. The following is a sample of what the datasets look like:
Image 1
Class Predicted TRUE
People 6 6
Cars 2 1
Image 2
Class Predicted TRUE
People 0 0
Cars 0 0
... and so on ...
Image 5000
Class Predicted TRUE
People 2 4
Cars 1 1
I am assuming that I cannot use a confusion matrix to assess the accuracy because I am dealing with class frequency data for each image. What approach can I take to assess the accuracy of the predicted vs true datasets?
machine-learning accuracy
asked 4 mins ago
BorealisBorealis
172212
$endgroup$
add a comment |
$begingroup$
I am working with a machine learning approach that counts 2 classes of objects in images: people and cars. I have a predicted dataset, which is the predicted output from the machine learning approach and a "true" dataset which is the result of a human going through each image and counting people and cars. The following is a sample of what the datasets look like:
Image 1
Class Predicted TRUE
People 6 6
Cars 2 1
Image 2
Class Predicted TRUE
People 0 0
Cars 0 0
... and so on ...
Image 5000
Class Predicted TRUE
People 2 4
Cars 1 1
I am assuming that I cannot use a confusion matrix to assess the accuracy because I am dealing with class frequency data for each image. What approach can I take to assess the accuracy of the predicted vs true datasets?
machine-learning accuracy
asked 4 mins ago
BorealisBorealis
172212
$endgroup$
I am working with a machine learning approach that counts 2 classes of objects in images: people and cars. I have a predicted dataset, which is the predicted output from the machine learning approach and a "true" dataset which is the result of a human going through each image and counting people and cars. The following is a sample of what the datasets look like:
Image 1
Class Predicted TRUE
People 6 6
Cars 2 1
Image 2
Class Predicted TRUE
People 0 0
Cars 0 0
... and so on ...
Image 5000
Class Predicted TRUE
People 2 4
Cars 1 1
I am assuming that I cannot use a confusion matrix to assess the accuracy because I am dealing with class frequency data for each image. What approach can I take to assess the accuracy of the predicted vs true datasets?
machine-learning accuracy
machine-learning accuracy
asked 4 mins ago
BorealisBorealis
172212
asked 4 mins ago
BorealisBorealis
172212
asked 4 mins ago
BorealisBorealis
172212
asked 4 mins ago
BorealisBorealis
172212
asked 4 mins ago
BorealisBorealis
172212
172212
add a comment |
add a comment |
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