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I need to classify the data using BayesNet in Python. I have used scikit learn for other classifiers like Random Forests, SVM etc. I know it has Naive Bayes but I am looking for Bayesian Network alone. If anyone could help me with it it would be very helpful Also, if there is an implementation of it for reference that would be even more helpful. Thanks
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Step 1: Calculate the prior probability for given class labels. Step 2: Find Likelihood probability with each attribute for each class. Step 3: Put these value in Bayes Formula and calculate posterior probability. Step 4: See which class has a higher probability, given the input belongs to the higher probability class.
A Bayesian network (also known as a Bayes network, belief network, or decision network) is a probabilistic graphical model that represents a set of variables and their conditional dependencies via a directed acyclic graph (DAG). Bayes' rule is used for inference in Bayesian networks, as will be shown below.
I see that there are many references to Bayes in scikit-learn API, such as Naive Bayes, Bayesian regression, BayesianGaussianMixture etc. On searching for python packages for Bayesian network I find bayespy and pgmpy.
1.9. Naive Bayes — scikit-learn 1.1.1 documentation 1.9. Naive Bayes ¶ Naive Bayes methods are a set of supervised learning algorithms based on applying Bayes’ theorem with the “naive” assumption of conditional independence between every pair of features given the value of the class variable.
Because scikit-learn has a Gaussian process module, we can implement this algorithm on top of the scikit-learn package. In pseudocode, the Bayesian optimization algorithm looks as follows:
If you wish to understand and use Bayesian networks, you can try OpenMarkov, an open-source tool. I recommend you having a look at its tutorial. Show activity on this post. PyMC3 is a Python library build on top of Theano. And then there pymc3_models that adds a scikit-learn like API.
You can use Weka for classify the data using BayesNet in Python.You can train your data using Weka and save your model as XML then you can write prediction API's in python for that saved model.
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