Naive Bayes I
One of the sub-fields of predictive modeling is supervised pattern classification; supervised pattern classification is the task of training a model based on labeled training data which then can be used to assign a pre-defined class label to new objects. One example that we will explore throughout this article is spam filtering via naive Bayes classifiers in order to predict whether a new text message can be categorized as spam or not-spam.
The probabilistic model of naive Bayes classifiers is based on Bayes’ theorem, and the adjective naive comes from the assumption that the features in a dataset are mutually independent.Especially for small sample sizes, naive Bayes classifiers can outperform the more powerful alternatives
We have to keep in mind that the type of data and the type problem to be solved dictate which classification model we want to choose. In practice, it is always recommended to compare different classification models on the particular dataset and consider the prediction performances as well as computational efficiency.
The probability model that was formulated by Thomas Bayes (1701-1761) is quite simple yet powerful;
Read full article from Naive Bayes I
One of the sub-fields of predictive modeling is supervised pattern classification; supervised pattern classification is the task of training a model based on labeled training data which then can be used to assign a pre-defined class label to new objects. One example that we will explore throughout this article is spam filtering via naive Bayes classifiers in order to predict whether a new text message can be categorized as spam or not-spam.
The probabilistic model of naive Bayes classifiers is based on Bayes’ theorem, and the adjective naive comes from the assumption that the features in a dataset are mutually independent.Especially for small sample sizes, naive Bayes classifiers can outperform the more powerful alternatives
We have to keep in mind that the type of data and the type problem to be solved dictate which classification model we want to choose. In practice, it is always recommended to compare different classification models on the particular dataset and consider the prediction performances as well as computational efficiency.
The probability model that was formulated by Thomas Bayes (1701-1761) is quite simple yet powerful;
The probability model that was formulated by Thomas Bayes (1701-1761) is quite simple yet powerful; it can be written down in simple words as follows:
| (1) |
The general notation of the posterior probability can be written as
Read full article from Naive Bayes I
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