CODEWITHSUNDEEP
machine-learningclassificationprobabilitynaivebayes
user1946217
user1946217

Reputation: 1753

Classification using Naive Bayes

I am trying to Classify a sample using Naive Bayes. My sample size is 2.8million records, 90% of the records have Class Label(dependent variable) = "0" and the rest have it as "1". The distribution in the testing set is also the same(90% - 10%) The Naive Bayes Classifier labels the entire testing set to "0". How do I deal with this case? Are there any other Algorithms which can be implemented in such cases.

Upvotes: 1

Views: 1003

Answers (3)

John Greenall
John Greenall

Reputation: 1690

Your problem may or may not be solved by using a better classifier. The issue here is that your problem is unbalanced. If the data is non-separable then 90% accuracy might represent good performance, which the classifier achieves by always making the same prediction. If this is not the behaviour you want, you should make use of a cost function or resample from your positives so that you have a more even number of positives.

Upvotes: 2

unique2
unique2

Reputation: 2302

Almost all classification methods actually don't return a binary result, but a propensity score (usually between 0 and 1) of how likely the given case falls within the category. Binary results are then created by picking a cut-off point, usually at .5.

When you want to identify rare cases using weak predictors any classification method may be unable to find cases with a propensity score higher than .5 resulting in all 0s as in your case.

There are 3 things you can do in such a situation:

  • I recommend finding stronger predictors if at all possible
  • A different statistical method like may be better at identifying patterns in your data set
  • Lowering the cut-off point will increase the number of true positives at the expense of more false positives

Upvotes: 0

lejlot
lejlot

Reputation: 66815

There are dozens of classifiers, including:

  • Logistic regression
  • SVM
  • Decision tree
  • Neural Network
  • Random forest
  • many, meny more...

most of which can handle class disproportions using some custom technique, for example in SVM it is a "class weighting" (avaliable in scikit-learn).

So why does NB fail? Naive Bayes is very Naive, it assumes independence of each feature, which is rarely the case, so it is just a simple idea to understand, but very weak classifier in general.

Upvotes: 0

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