![]() This is particularly true in Text Classification problems where our features are usually words which obviously are not independent. Moreover Maximum Entropy classifier is used when we can’t assume the conditional independence of the features. When to use the Ma圎nt Text Classifier?ĭue to the minimum assumptions that the Maximum Entropy classifier makes, we regularly use it when we don’t know anything about the prior distributions and when it is unsafe to make any such assumptions. The Max Entropy classifier can be used to solve a large variety of text classification problems such as language detection, topic classification, sentiment analysis and more. The Ma圎nt is based on the Principle of Maximum Entropy and from all the models that fit our training data, selects the one which has the largest entropy. ![]() Unlike the Naive Bayes classifier that we discussed in the previous article, the Max Entropy does not assume that the features are conditionally independent of each other. ![]() The Max Entropy classifier is a probabilistic classifier which belongs to the class of exponential models. Note that Max Entropy classifier performs very well for several Text Classification problems such as Sentiment Analysis and it is one of the classifiers that is commonly used to power up our Machine Learning API. Check out the package .classification to see the implementation of Max Entropy Classifier in Java. Update: The Datumbox Machine Learning Framework is now open-source and free to download. Implementing Max Entropy in a standard programming language such as JAVA, C++ or PHP is non-trivial primarily due to the numerical optimization problem that one should solve in order to estimate the weights of the model. The Max Entropy classifier is a discriminative classifier commonly used in Natural Language Processing, Speech and Information Retrieval problems. In this tutorial we will discuss about Maximum Entropy text classifier, also known as Ma圎nt classifier.
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