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  The article studies the advantage of Support Vector Regression (SVR) over Simple Linear Regression (SLR) models for predicting real values, using the same basic idea as Support Vector Machines (SVM) use for classification. The article studies the advantage of Support Vector Regression (SVR) over Simple Linear Regression (SLR) models. SVR uses the same basic idea as Support Vector Machine (SVM), a classification algorithm, but applies it to predict real values rather than a class. SVR acknowledges the presence of non-linearity in the data and provides a proficient prediction model. Along with the thorough understanding of SVR, we also provide the reader with hands on experience of preparing the model on R. We perform SLR and SVR on the same dataset and make a comparison. The article is organized as follows; Section 1 provides a quick review of SLR and its implementation on R. Section 2 discusses the theoretical aspects of SVR and the steps to fit SVR on R. It also covers the basic...

  Reinforcement Learning with R Machine learning algorithms were mainly divided into   three   main categories. Supervised learning algorithms Classification and regression algorithms Unsupervised learning algorithms Clustering algorithms Reinforcement learning algorithms We have covered   supervised learning and unsupervised learning   algorithms couple of times in our blog articles. In this article, you are going to learn about the third category of machine learning algorithms. Which are reinforcement learning algorithms. Before we drive further let quickly look at the table of contents. Table of contents: Reinforcement learning real-life example Typical reinforcement process Reinforcement learning process Divide and Rule Reinforcement learning implementation in R Preimplementation background MDP toolbox package Using Github reinforcement learning package How to change environment Complete code Conclusion Related courses Practical Reinforcement learning Reinfo...