Fitting model to training data
It's time to split your data into a training set to fit a model and a separate test set to evaluate the predictive power of the model. Before making this split however, we first sample 100% of the rows of house_prices without replacement and assign this to house_prices_shuffled. This has the effect of "shuffling" the rows, thereby ensuring that the training and test sets are randomly sampled.
Questo esercizio fa parte del corso
Modeling with Data in the Tidyverse
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# Set random number generator seed value for reproducibility
set.seed(76)
# Randomly reorder the rows
house_prices_shuffled <- house_prices %>%
sample_frac(size = 1, replace = FALSE)
# Train/test split
train <- house_prices_shuffled %>%
slice(___:___)
test <- house_prices_shuffled %>%
slice(___:___)