Machine Learning Tutorial: The Naive Bayes Text Classifier | Datumbox
Naive Bayes is one of the simplest classifiers that one can use because of the simple mathematics that are involved and due to the fact that it is easy to code with every standard programming language including PHP, C#, JAVA etc.
What is the Naive Bayes Classifier?
The Naive Bayes classifier is a simple probabilistic classifier which is based on Bayes theorem with strong and naïve independence assumptions. It is one of the most basic text classification techniques with various applications in email spam detection, personal email sorting, document categorization, sexually explicit content detection, language detection and sentiment detection. Despite the naïve design and oversimplified assumptions that this technique uses, Naive Bayes performs well in many complex real-world problems.
Even though it is often outperformed by other techniques such as boosted trees, random forests, Max Entropy, Support Vector Machines etc, Naive Bayes classifier is very efficient since it is less computationally intensive (in both CPU and memory) and it requires a small amount of training data. Moreover, the training time with Naive Bayes is significantly smaller as opposed to alternative methods.
You can use Naive Bayes when you have limited resources in terms of CPU and Memory. Moreover when the training time is a crucial factor, Naive Bayes comes handy since it can be trained very quickly. Indeed Naive Bayes is usually outperformed by other classifiers, but not always! Make sure you test it before you exclude it from your research. Keep in mind that the Naive Bayes classifier is used as a baseline in many researches.
Which Naive Bayes Variation to use?
There are several Naive Bayes Variations. Here we will discuss about 3 of them: the Multinomial Naive Bayes, the Binarized Multinomial Naive Bayes and the Bernoulli Naive Bayes. Note that each can deliver completely different results since they use completely different models.
Usually Multinomial Naive Bayes is used when the multiple occurrences of the words matter a lot in the classification problem. Such an example is when we try to perform Topic Classification. The Binarized Multinomial Naive Bayes is used when the frequencies of the words don’t play a key role in our classification. Such an example is Sentiment Analysis, where it does not really matter how many times someone mentions the word “bad” but rather only the fact that he does. Finally the Bernoulli Naive Bayes can be used when in our problem the absence of a particular word matters. For example Bernoulli is commonly used in Spam or Adult Content Detection with very good results.
the Naive Bayes classifier assumes that the features used in the classification are independent. Despite the fact that this assumption is usually false, analysis of the Bayesian classification problem has shown that there are some theoretical reasons for the apparently unreasonable efficacy of Naive Bayes classifiers as Zhang (2004) shown.
Read full article from Machine Learning Tutorial: The Naive Bayes Text Classifier | Datumbox
Naive Bayes is one of the simplest classifiers that one can use because of the simple mathematics that are involved and due to the fact that it is easy to code with every standard programming language including PHP, C#, JAVA etc.
What is the Naive Bayes Classifier?
The Naive Bayes classifier is a simple probabilistic classifier which is based on Bayes theorem with strong and naïve independence assumptions. It is one of the most basic text classification techniques with various applications in email spam detection, personal email sorting, document categorization, sexually explicit content detection, language detection and sentiment detection. Despite the naïve design and oversimplified assumptions that this technique uses, Naive Bayes performs well in many complex real-world problems.
Even though it is often outperformed by other techniques such as boosted trees, random forests, Max Entropy, Support Vector Machines etc, Naive Bayes classifier is very efficient since it is less computationally intensive (in both CPU and memory) and it requires a small amount of training data. Moreover, the training time with Naive Bayes is significantly smaller as opposed to alternative methods.
You can use Naive Bayes when you have limited resources in terms of CPU and Memory. Moreover when the training time is a crucial factor, Naive Bayes comes handy since it can be trained very quickly. Indeed Naive Bayes is usually outperformed by other classifiers, but not always! Make sure you test it before you exclude it from your research. Keep in mind that the Naive Bayes classifier is used as a baseline in many researches.
Which Naive Bayes Variation to use?
There are several Naive Bayes Variations. Here we will discuss about 3 of them: the Multinomial Naive Bayes, the Binarized Multinomial Naive Bayes and the Bernoulli Naive Bayes. Note that each can deliver completely different results since they use completely different models.
Usually Multinomial Naive Bayes is used when the multiple occurrences of the words matter a lot in the classification problem. Such an example is when we try to perform Topic Classification. The Binarized Multinomial Naive Bayes is used when the frequencies of the words don’t play a key role in our classification. Such an example is Sentiment Analysis, where it does not really matter how many times someone mentions the word “bad” but rather only the fact that he does. Finally the Bernoulli Naive Bayes can be used when in our problem the absence of a particular word matters. For example Bernoulli is commonly used in Spam or Adult Content Detection with very good results.
the Naive Bayes classifier assumes that the features used in the classification are independent. Despite the fact that this assumption is usually false, analysis of the Bayesian classification problem has shown that there are some theoretical reasons for the apparently unreasonable efficacy of Naive Bayes classifiers as Zhang (2004) shown.
Read full article from Machine Learning Tutorial: The Naive Bayes Text Classifier | Datumbox
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