def NBAccuracy(features_train, labels_train, features_test, labels_test):
""" compute the accuracy of your Naive Bayes classifier """
### import the sklearn module for GaussianNB
from sklearn.naive_bayes import GaussianNB
from sklearn.metrics import accuracy_score
### create classifier
clf = GaussianNB() #TODO
### fit the classifier on the training features and labels
clf.fit(features_train, labels_train)#TODO
### use the trained classifier to predict labels for the test features
pred = clf.predict([[-0.8, -1]])#TODO
### calculate and return the accuracy on the test data
### this is slightly different than the example,
### where we just print the accuracy
### you might need to import an sklearn module
accuracy = clf.accuracy_score(features_train, labels_train)#TODO
return accuracy
"""
import sys
from time import time
sys.path.append("../tools/")
from email_preprocess import preprocess
from sklearn.naive_bayes import GaussianNB
from sklearn.sklearn.metrics import accuracy_score
### features_train and features_test are the features for the training
### and testing datasets, respectively
### labels_train and labels_test are the corresponding item labels
features_train, features_test, labels_train, labels_test = preprocess()
#########################################################
### your code goes here ###
t0 = time.time()
cls = GaussianNB()
cls.fit(features_train, labels_test)
t1 = time.time()
time_to_fitness = t1 - t0
accuracy = cls.accuracy_score(features_test, labels_test)
t2 = time.time()
print(
"Achieved accuracy {} in time {} ({} seconds to fit the classifier, {} seconds to test accuracy"
.format(accuracy, t2 - t0, time_to_fitness, t2 - t1))
return cls
#########################################################
