#Caret train series#

If our features and the outcome are linearly related, we may start with linear regression models. This step can be important as it helps us to select appropriate models. # There is no missing data in all of the columnsĠ Visualize relationship between variablesīefore we perform any modeling, it is a good idea to explore correlations between the predictors and the predictand. # Size of DataFrameĪre there any missing values in any of the columns? map(power_plant, ~sum(is.na(.))) # using purrr package Power Output is the value we are trying to predict given the measurements above. Message('The class is now ', class(power_plant)) # so let's change the data to simple data frame 'tbl_df' 'tbl' 'ame' # Caret faces problems working with tbl, This is a step that we should always perform before trying to fit a model to the data, as this step will often lead to important insights about our data. Library(tidyverse) Load Data power_plant = read_excel('Folds5x2_pp.xlsx') Exploratory Data Analysis (EDA) The caret package (short for _C_lassification _A_nd _RE_gression _T_raining) is a set of functions that attempt to streamline the process for creating predictive models. The task is a regression problem since the label (or target) we are trying to predict is numeric. You can find more details about the dataset on the UCI page.

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 Repository Combined Cycle Power Plant Data Set. Next, we will see the other non-linear regression models. In the second part of the post, we will work with regularized linear regression models (ridge, lasso and elasticnet). We will predict power output given a set of environmental readings from various sensors in a natural gas-fired power generation plant. 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.

#Caret train series#

This blog post series is on machine learning with R.