It's not at all only recognized for its simplicity, but will also for its performance. It is actually rapid to create products and make predictions with Naive Bayes algorithm.
This happens because Machine Learning Algorithms tend to be intended to increase accuracy by lowering the mistake. Consequently, they don't take into consideration The category distribution / proportion or balance of classes.
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Example: I have an unknown fruit that is certainly yellow in color, 5.5 inches long, diameter of the inch, and density of X. What fruit is this? I would use classification for this type of problem to classify it like a banana (versus an apple or orange).
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The e book is structured as a series of survey content articles on the principle present-day sub-fields of reinforcement learning, including partially observable environments, hierarchical undertaking decompositions, relational awareness illustration and predictive condition representations.
Reinforcement Learning Full Tutorial