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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.

Cet exercice fait partie du cours

Modeling with Data in the Tidyverse

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Exercice interactif pratique

Essayez cet exercice en complétant cet exemple de code.

# 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(___:___)
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