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Machine Learning with R Caret – Part 1

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This blog post series is on machine learning with R. We will use the Caret package in R. In this part, we will first perform exploratory Data Analysis (EDA) on a real-world dataset, and then apply non-regularized linear regression to solve a supervised regression problem on the dataset. We will predict power output given a set of environmental readings from various sensors in a natural gas-fired power generation plant. In the second part of the post, we will work with regularized linear regression models (ridge, lasso and elasticnet). Next, we will see the other non-linear regression models. The real-world data we are using in this post consists of 9,568 data points, each with 4 environmental attributes collected from a Combined Cycle Power Plant over 6 years (2006-2011), and is provided by the University of California, Irvine at UCI Machine Learning…
Original Post: Machine Learning with R Caret – Part 1