In this article, we looked at one of the supervised machine learning algorithm “Naive Bayes” mainly used for classification. Congrats, if you’ve thoroughly & understood this article, you’ve already taken you first step to master this algorithm. From here, all you need is practice. Further...
Apart from its advantages, the naive Bayes classification algorithm also has some drawbacks. The algorithm assumes that the attributes of the training dataset are independent of each other. This assumption is not always True. Hence, when there is a correlation between two attributes in a given tra...
Nevertheless, the Naive Bayes algorithm has been shown time and time again to perform really well in classification problems, despite the assumption of independence. Simultaneously, it is a fast algorithm since it scales easily to include many predictors without having to handle multi-dimensional corre...
In this article, I’ll explain the basics of this algorithm, so that next time when you come across large data sets, you can bring this algorithm to action. In addition, if you are a newbie in Python, you should be overwhelmed by the presence of available codes in this ...
Algorithm Bayes theorem provides a way of calculating the posterior probability,P(c|x), fromP(c),P(x), andP(x|c). Naive Bayes classifier assume that the effect of the value of a predictor (x) on a given class (c) is independent of the values of other predictors. This assumption is...
Naive Bayes algorithm Process Flow Take an example, Imagine because of current weather, cricket match will happen or not? Now, we need to classify whether players will play the match or not based on weather conditions. Convert the data set into a frequency table ...
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The algorithm is based on Bayes' theorem, which is a probability theory that relates the probability of an event based on prior knowledge of conditions that might be related to the event. The formula for Bayes' theorem is: In this equation, ‘A’ stands for class, and ‘B’ stands for ...
In the last decade the standard Naive Bayes (SNB) algorithm has been widely employed in multi–class classification problems in cheminformatics. This popularity is mainly due to the fact that the algorithm is simple to implement and in many cases yields respectable classification results. Using cleve...
Because the ultimate goal of any machine learning algorithm is to generalize to data not seen during training, we always evaluate the accuracy of our model on a separate data set that was not used during training. 3. Naive Bayes An extensive review of the Naive Bayes classifier is beyond the...