Similarity learning is an area of supervised machine learning closely related to regression and classification, but the goal is to learn from examples using a similarity function that measures how similar or related two objects are.
Based on the selected features, cyber threat classification analysis is performed using seven state-of-the-art ML classification algorithms. The results indicate the proposed evidence-based feature selection method performs better, or, at least as good, to the state-of-the-art.
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Tags: Classification in Machine Learning Classification Using Regression.
In this article, we'll explore binary classification using TensorFlow, one of the most popular deep learning libraries. Before getting into the Binary Classification, let's discuss a little about classification problem in Machine Learning.
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Various supervised learning algorithms such as Support Vector Machine, k-Nearest Neighbors, Decision Tree, and Random Forest are implemented and analyzed. Feature scaling and model tuning techniques are used to improve the classification performance and reduce overfitting.
Finally, we used labeled data to train the supervised machine learning models for the prediction of fault prone software modules. Moreover, to build an effective prediction model, we used genetic algorithm to search those sets of metrics which are highly correlated with severe bugs.
