imputer_object = imp(missing_values='NaN', strategy='mean', axis=0)
# fitting the object on our data -- we do this so that we can save the 
# fit for our new data.
imputer_object.fit(explanatory_df)
explanatory_df = imputer_object.transform(explanatory_df)


##########################
### Naive Bayes Model  ###
##########################


### creating naive bayes classifier ###

naive_bayes_classifier = nb()

accuracy_scores = cv(naive_bayes_classifier, explanatory_df, response_series, cv=10, scoring='accuracy')
print accuracy_scores.mean()
#looks like on average the model is 60% accurate, not very high

### calculating accuracy metrics for comparison ###

## ACCURACY METRIC 1: Cohen's Kappa ##

mean_accuracy_score = accuracy_scores.mean()
largest_class_percent_of_total = response_series.value_counts(normalize = True)[0]

largest_class_percent_of_total
#the largest class percent total is 90%, thus the model will correctly
#predict 90% of the time that someone WILL NOT be in the hall of fame
Example #2
0
def naive_bayes(data, classifiers):
    bayes = nb()
    return bayes.fit(data, classifiers)