In [19]:
# For Classification
import scikitplot as skplt
from sklearn.metrics import accuracy_score, classification_report
from sklearn.metrics import cohen_kappa_score, confusion_matrix
# Predict with the best model.
predicted_y = clf.predict(X_test, verbose=0)
# Evaluate the skill of the Trained model
acc = accuracy_score(y_test, predicted_y)
classReport = classification_report(y_test, predicted_y)
confMatrix = confusion_matrix(y_test, predicted_y)
print(); print('Testing Results of the trained model: ')
print(); print('Accuracy : ', acc)
#print(); print('Confusion Matrix :\n', confMatrix)
print(); print('Classification Report :\n',classReport)
# Confusion matrix
skplt.metrics.plot_confusion_matrix(y_test, predicted_y,figsize=(11, 11)); plt.show()
Testing Results of the trained model:
Accuracy : 0.96
Classification Report :
precision recall f1-score support
0 0.95 1.00 0.97 36
1 0.92 1.00 0.96 47
2 1.00 1.00 1.00 46
3 0.94 0.89 0.91 36
4 0.94 1.00 0.97 34
5 0.91 0.91 0.91 34
6 1.00 0.94 0.97 33
7 0.98 0.95 0.97 44
8 1.00 0.94 0.97 48
9 0.95 0.95 0.95 42
accuracy 0.96 400
macro avg 0.96 0.96 0.96 400
weighted avg 0.96 0.96 0.96 400
