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How accurate is naive Bayes?
5 is 81.91%, for Naive-Bayes it is 81.69%, and for NBTree it is 84.47%. Absolute differences do not tell the whole story be- cause the accuracies may be close to 100% in some cases. Increasing the accuracy of medical diagnosis from 98% to 99% may cut costs by half because the number of errors is halved.
Why Naive Bayes works very well with many number of features?
Because of the class independence assumption, naive Bayes classifiers can quickly learn to use high dimensional features with limited training data compared to more sophisticated methods. This can be useful in situations where the dataset is small compared to the number of features, such as images or texts.
What can naive Bayes be used for?
Naive Bayes is a probabilistic machine learning algorithm that can be used in a wide variety of classification tasks. Typical applications include filtering spam, classifying documents, sentiment prediction etc. It is based on the works of Rev.
How to use naive Bayes to predict fraudsters?
I am trying to use a naive Bayes classification technique to predict fraudsters ( Caller ). My training set of 138 instances has 5 columns viz. Morning, Afternoon, Evening, Night and Caller. Morning has 8 names; the rest all have 3.
When to use cross validation in naive Bayes?
In your case, you want to make as much use of your limited data as possible so I suggest reading up on cross-validation. You would want to use cross-validation to select the best model (and tune the parameters within the Naive Bayes, if that’s what you are set on using) by calculating the error within each fold.
What’s the accuracy rate of a Bayes model?
Before starting any sort of parameter tuning or model selection, you must separate your labeled data into a training and test set. Usually we see something such as 80/20 or 90/10 (sometimes even 50/50 for huge datasets) for test/train respectively.
What is the purpose of threshold in naive Bayes algorithm?
When using accuracy as a metric you essentially count the amount of correct classifications and thus state a definite threshold (like 50%) that is used to determine which class is being predicted for each sample. You might want to take a look at this answer, and Frank Harrell’s Classification vs. Prediction.