Hey guys,
I’m working in a personal project which is a simple classifier 0 or 1.
I have traied two different algorithms.
First of all Logistic Regression which obtains the following results:
precision recall f1-score support
0.0 0.78 0.70 0.73 417
1.0 0.67 0.76 0.71 342
accuracy 0.72 759
macro avg 0.72 0.73 0.72 759
weighted avg 0.73 0.72 0.72 759
Confusion Matrix
[[290 127]
[ 83 259]]
Next I splited again the dataset and I tried with the SVM algorithm.
the problem is that when I do the predictions with the x_test splited dataset all values are classified as 0.0.
Here I the confusion matrix and the classification report obtained for this algorithm.
precision recall f1-score support
0.0 0.55 1.00 0.71 417
1.0 0.00 0.00 0.00 342
accuracy 0.55 759
macro avg 0.27 0.50 0.35 759
weighted avg 0.30 0.55 0.39 759
Confusion Matrix
[[417 0]
[342 0]]
The follwing warning I supose it occurs because is dividing the 0.00 with the 0.55.
What may be happening? I suppose it’s a comon error.