R Tutorial: The modeling problem for prediction

Want to learn more? Take the full course at https://learn.datacamp.com/courses/modeling-with-data-in-the-tidyverse at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- You’ll finish this first chapter by studying the modeling problem for prediction. While the mechanics for predictive modeling might be similar to those for explanatory modeling, you'll see there are some subtle differences in the goals. Recall the modeling for explanation problem: that the f and epsilon components are unknown and that you only observe the y and x components. Based on y and x, you fit a model f-hat that hopefully closely approximates the true f while ignoring the error epsilon. In other words you want the fitted model to separate the signal from the noise. You then use this fitted model to obtain fitted/predicted values of y called y-hat. In explanatory modeling, the form of f-hat matters greatly, in particular any values that quantify the relationship between y and x. For example, for every increase in 1 in instructor age, what is the typical associated change in teaching score? However, in predictive modeling, you don't care so much about the form of f-hat, but more that it yields good predictions. So, if I give inputs x to f-hat, can I get a prediction y-hat that is close to the true value of y? Let's build our intuition about predictive modeling through a further EDA of house prices. However, instead of using a numerical explanatory variable, let's use a categorical predictor variable, house condition. Let's glimpse() just the variables price and condition. condition is a categorical variable with 5 levels, where 1 indicates poor and 5 indicates excellent. Note that while condition is a number between 1 and 5, observe they are represented in R as fct, or factors, so they are treated as categorical. Since the original price variable was right-skewed, recall you applied a log10-transformation to unskew them. Now, how