Bayesian Network is a kind of Graphical model called a Directed Acyclic Graph or DAG. It means all the edges (arcs) in the graph are directed, and there are no cycles.
In the following article, I introduce some of the basic concepts of the Bayesian Network, which comes from the probability theory. I continue with the definitions of probability theory, joint probability distribution, and graph theory in the Bayesian Network.
In contrast to training a deep model from scratch which needs an enormous amount of training and test data, computational time, and budgets, there are pre-trained models, which we can use for different purposes.
In the following post, we will learn about the nature of the convolutional neural networks and their layers and a short example of defining transfer learning models in python with the use of the Keras library;
eature selection is the procedure of selecting proper features as the machine learning model inputs.
The feature selection process is divided into supervised (considering the target feature) and unsupervised (ignoring the target) approaches. Moreover, the supervised model includes various strategies, for instance;
In this article, I will introduce the Bayesian Network and basic concepts with related equations and examples.
Bayesian network is a graphical model (DAG — Directed Acyclic Graph)based on the probability definitions and reviews the conditional dependencies between the variables and events (chance of happening an event).
Identifying Antibiotic-Resistant for different Bacterian diseases is one of the most interesting real-world problems, which in most cases is a binary classification with zero and one value as the target (whether there is a resistance or not).
If we want to solve this problem with machine learning models, we need a clean dataset with class labels, which would be a supervised binary classification problem. In this study, I will introduce an example of this problem and try to predict the resistance with a classifier.
Computer science
Machine learning
Posting about some interesting tips in coding, developing machine learning models, and statistical analysis. I also try to solve some of the common issues in the python and R environment and share them.
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