NLP Learnings 16 – Language Models – Classifiers – What are Probabilistic Classifiers

AI & ML & Blockchain, Blog, Machine Learning

In the previous post, we learned the classifier definition. In this post, we learn the definition of Probabilistic Classifiers and in the next post, we learn Naive Bayes and Linear classifiers. These two are probabilistic classifiers.

What are Probabilistic Classifiers?

For given input data or document(s), a Probabilistic Classifier is an equation or a classifier that can predict a probability distribution over a set of categories or classes in the input document(s). As mentioned in the previous post, the categories or classes could be Politics, Sports, Entertainment, Science, Technology, Films, etc.

What do Probabilistic Classifiers do?

For the given input data document(s), probabilistic classifiers do prediction of the categories or classes of a document or sentences or phrases or a combination of words or words.

How do you evaluate the outputs of Probabilistic Classifiers?

As mentioned above, Probabilistic Classifiers do “prediction of categories or classes” of given input document(s).

Loss Functions

Whatever the prediction is done, whether they are near to reality or true prediction (in the context of your business or personal need) can be evaluated through another function. These other functions are called Loss Functions.

Commonly used loss functions for probabilistic classification are log loss and the Brier score between the predicted and the true probability distributions. 

 

Related Topics:

  1. Natural Language Processing Primer
  2. NLP Learnings 02 – After Primer – NLP Models & Evaluating NLP Models
  3. NLP Learnings 03 – What is Linguistic in NLP and Linguistic Categories or Linguistic Levels
  4. NLP Learnings 04 – Sentence Linguistic Analysis – Words As First Step
  5. NLP Learnings 05 – Sentence Linguistic Analysis – Pragmatics Analysis
  6. NLP Learnings 06 – What can we do with A Sentence in NLP Tasks
  7. NLP Learnings 07 – Introducing NLP Language Models
  8. NLP Learnings 08 – Language Models – Probability Types
  9. NLP Learnings 09 – Language Models – Measuring Text Based On Probability
  10. NLP Learnings 10 – Language Models – Defining Word Boundaries
  11. NLP Learnings 11 – Language Models – Your Business Text Data and Their Words Representation
  12. NLP Learnings 12 – Language Models – Regularization Techniques And Your Business Text Data
  13. NLP Learnings 13 – Language Models – Smoothing Regularization Techniques And Your Business Text Data
  14. NLP Learnings 14 – Language Models – NLP Key Terms and Concepts
  15. NLP Learnings 15 – Language Models – Classifiers Introduction
  16. NLP Learnings 16 – Language Models – Classifiers – What are Probabilistic Classifiers
  17. NLP Learnings 17 – Language Models – Classifiers – Simple Classifiers Introduction
  18. NLP Learnings 18 – Language Models – Classifiers – Simple Classifiers – Linear Probabilistic Classifier Introduction
  19. NLP Learnings 19 – Language Models – Classifiers – Evaluating Classifiers – Precision Recall F-Score Confusion Matrix
  20. NLP Learnings 20 – Language Models – Classifiers – Represent Words In A Document – Choosing and Representing Features In The Right Way
  21. NLP Learnings 21 – Language Models – Word Embeddings
  22. NLP Learnings 22 – Language Models – Sentence and Document Embeddings
  23. NLP Learnings 23 – Language Models – Sentence and Document Embeddings – Which one to consider
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