eval = Evaluation(bayes_case=bayes_classifier,
data_prep=data_preprocess,
test_size=0.30)
class_id = 1 # The class_id for the required class
# Returns confusion matrix for a given Bayesian Classifier Case
cm = eval.confusion_matrix()
# Returns the accuracy of classification for a given Bayesian Classifier Case
acc = eval.accuracy()
# Returns the precision for a given class for a given Bayesian Classifier Case
prec = eval.precision(class_id)
# Returns the recall for a given class for a given Bayesian Classifier Case
rec = eval.recall(class_id)
# Returns the F-score for a given class for a given Bayesian Classifier Case
f_score = eval.f_score(class_id)
# Returns the mean precision of classification for a given Bayesian Classifier Case
mean_prec = eval.mean_precision()
# Returns the mean recall of classification for a given Bayesian Classifier Case
mean_rec = eval.mean_recall()
# Returns the mean F-score of classification for a given Bayesian Classifier Case
mean_f_score = eval.mean_f_score()
# Plots the confusion matrix of classification for a given Bayesian Classifier Case
eval.plot_confusion_matrix()
