Warning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter

user_16047726 0
Viewed 6021

can anyone help me solve this error:

Warning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use zero_division parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result))

the error appears when I add Adam's tuning parameter.

#tunning parameter from keras.optimizers import Adam
optimize = Adam(learning_rate=0.00001,beta_1=0.9,beta_2=0.99)
model.compile(optimizer=optimize,loss='categorical_crossentropy', metrics=['accuracy'])

does anyone understand the error of this code?

from sklearn.metrics import confusion_matrix, classification_report
prediksi = model.predict(test_data_generator)
y_pred = np.argmax(prediksi, axis=1)
print(confusion_matrix(test_data_generator.classes,y_pred))
print(classification_report(test_data_generator.classes,y_pred))

I've also tried using labels=np.unique(y_pred) but the results do not show the value of the accuracy of

scikit-learn jupyter-notebook python
1 Answers

This warning occurs because y_true contains labels that are not present in your predictions (y_pred) like in the example below:

import numpy as np
from sklearn.metrics import confusion_matrix, classification_report

y_pred = np.ones(10,)
y_true = np.ones(10,)
y_true[0]=0
print(confusion_matrix(y_true,y_pred))
print(classification_report(y_true,y_pred))

You can remove this warning by setting classification_report argumentzero_division=1.

But it is not wise as it shows you that there is a problem with your classifier.

user_45746330
Related