Naive Bayes is naive because it assumes every feature is independent in predicting the class. Naive Bayes classifiers makes the naive assumption that the features are independent. Lecture 12: Naïve Bayes Classifier, Evaluation Methods Ling 1330/2330 Computational Linguistics Na-Rae Han, 10/3/2019 . This time I want to demonstrate how all this can be implemented using WEKA application. Overview 10/3/2019 2 Text classification; Naïve Bayes classifier Language and Computers: Ch.5 Classifying documents NLTK book: Ch.6 Learning to classify text Evaluating the performance of a system Language and Computers: Ch.5.4 Measuring success, 5.4.1 Base rates … For those who don’t know what WEKA is I highly recommend visiting their website and getting the latest release. Naive Bayes is a Supervised Machine Learning algorithm based on the Bayes Theorem that is used to solve classification problems by following a probabilistic approach.

And the Machine Learning – The Naïve Bayes Classifier. The goal is to develop a Naïve Bayes classification model that identifies and flags insincere questions. In simple terms, a Naive Bayes classifier assumes that the presence of a particular feature in a class is unrelated to the presence of any other feature. Naive Bayes requires a small amount of training data to estimate the test data. This doesn't make sense, because the type of prediction variable should be ndarray - this is a return type of a predict() method of a Naive Bayes classifier in scikit-learn.. Also, later in your code you are tring to append a string to an integer (tweet_pred, which was redefined in a for-loop). If Naive Bayes is implemented correctly, I don't think it should be overfitting like this on a task that it's considered appropriate for (text classification). Naive Bayes has shown to perform well on document classification, but that doesn't mean that it cannot overfit data. While this is most likely not true in reality, it provides the benefit of quick and simple calculations that allow a Naive Bayes classifier to work well on problems such as text classification. Naive Bayes has shown to perform well on document classification, but that doesn't mean that it cannot overfit data. Naive Bayes is a classification technique based on an assumption of independence between predictors or what’s known as Bayes’ theorem. Bayes theorem gives the conditional probability of an event A given another event B has occurred. Theory. It is a compelling machine learning software written in Java. What are the Advantages and Disadvantages of Naïve Bayes Classifier? 3. This is a followup post from previous where we were calculating Naive Bayes prediction on the given data set. So, the training period is less. A practical explanation of a Naive Bayes classifier The simplest solutions are usually the most powerful ones, and Naive Bayes is a good example of that. Although Naive Bayes is a very fast and simple classifier, but there is some disadvantages that may degrade its work: 1- It assumes the attributes are independent. ... Browse other questions tagged naive-bayes or ask your own question. The difference between the Bayes Classifier and The Naive Bayes Classifier? Is Naive Bayes overfitting to the training set? Text classification/ Spam Filtering/ Sentiment Analysis: Naive Bayes classifiers mostly used in text classification (due to better result in multi class problems and independence rule) have higher success rate as compared to other algorithms. The Naive Bayes algorithm is called “Naive” because it makes the assumption that the occurrence of a certain feature is independent of the occurrence of other features. When assumption of independent predictors holds true, a Naive Bayes classifier performs better as compared to other models. It is based on the idea that the predictor variables in a Machine Learning model are independent of each other. Therefore, it is more proper to call Simple Bayes or Independence Bayes. A key challenge is to weed out insincere questions — those founded upon false premises, or that intend to make a statement rather than look for helpful answers. 1.

In spite of the great advances of the Machine Learning in the last years, it has proven to not only be simple but also fast, accurate, and reliable. This algorithm has been studied extensively since 1960s. Calculate probability for each word in a text and filter the words which have a probability less than threshold probability. On Quora, people can ask questions and connect with others who contribute unique insights and quality answers.

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