metrics AUC FNR FPR error_test error_train
classifier attack % poisoned GD method
adaline empty 0.0 mini-batch 0.94 0.00 0.12 0.03 0.01
stochastic 0.58 0.65 0.20 0.54 0.51
batch 0.93 0.00 0.14 0.03 0.01
0.5 mini-batch 0.92 0.01 0.15 0.04 0.01
stochastic 0.45 0.57 0.53 0.56 0.29
batch 0.90 0.02 0.19 0.06 0.00
logistic regression empty 0.0 mini-batch 0.95 0.01 0.09 0.03 0.00
stochastic 0.95 0.05 0.06 0.05 0.02
batch 0.97 0.01 0.04 0.02 0.00
0.5 mini-batch 0.95 0.01 0.08 0.03 0.00
stochastic 0.93 0.03 0.11 0.05 0.01
batch 0.94 0.01 0.11 0.03 0.00
Last active
August 11, 2016 00:00
-
-
Save galvanic/e185bd316de11ace5d62095ecc121aa4 to your computer and use it in GitHub Desktop.
test to see Adaline classifier performance depending on gradient descent mini-batch size (here 10) - vs. full batch and stochastic (batch size of 1)
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment