100% precision in training set with SVM classifier
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Hello! I have a Machine Learning doubt:
I am training a SVM classifier to classify binary images (70x70) in two classes. The dataset has 100 images/class.
In traning and test set the precision is 100%.
This makes me verify again the code and I make another experience: adding noise to the images e classifying again. With random images (100% noise) the precision in the training set was 100% e 50% approx.
The 100% training set precision with 100% noise is possible?
In the graph: Precision on test set
x-axis => noise;
y-axis => accuracy;
blue line => training the SVM classifier with noise
green line => training the SVM classifier without noise
4 comentarios
Walter Roberson
el 12 de Mzo. de 2018
You can get 100% accuracy in training of noise if you overfit. If your number of neurons is higher than the number of training datasets then it could potentially "remember" something characteristic of each data set.
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