China 
Africa 
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Exploratory Visualizations
Published in Max Kuhn, Kjell Johnson, Feature Engineering and Selection, 2019
These data will be analyzed in several chapters. Given the range of the daily ridership numbers, there was some question as to whether the outcome should be modeled in the natural units or on the log scale. On one hand, the natural units make interpretation of the results easier since the RMSE would be in terms of riders. However, if the outcome were transformed prior to modeling, it would ensure that negative ridership could not be predicted. The bimodal nature of these data, as well as distributions of ridership for each year that have a longer tail on the right made this decision difficult. In the end, a handful of models were fit both ways to make the determination. The models computed in the natural units appeared to have slightly better performance and, for this reason, all models were analyzed in the natural units.
Parametric estimation for AWJ cutting of Ti-6Al-4V alloy using Rat swarm optimization algorithm
Published in Materials and Manufacturing Processes, 2022
A. Tamilarasan, A. Renugambal, D. Vijayan
where ε is a variable that denotes additional sources of variability not addressed by Y. Measurement error, background noise, the influence of other factors, and so on are usually included. Typically, ε is treated as statistical error, with the assumption that it has a normal distribution with mean zero and variance σ2. Since they are expressed in natural units of measurement, the variables X1,X2, …,Xn are commonly referred to as natural variables. It is convenient in RSM to convert natural variables to coded variables x1,x2, … , xn that are typically described as dimensionless with mean zero and the same standard deviation. The response function is written as f(x1,x2, … , xn) with regard to the coded variables, also called the response surface.[19] The nature of the relationship between the dependent and independent variables is uncertain. The first step of RSM is therefore to find an appropriate approach to the true functional relationship of y with the series of separate variables used. Generally, RSM employs a second-order model, as seen below.[20]