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The following chunk will set up your document. Run it, then ignore it.
If the system prompts you to install a package, or gives you a “package not found” error, simply run install.packages("packagename") once to install it.
We will be using the flights data set from the nycflights13 package. nycflights13 is an R data package containing all out-bound flights from NYC.
glimpse(flights)
We will build a classification model that sees if any given flight is delayed or not. Furthermore, let us trim down the number of variables we are working with. Lastly, let us select to only work with flights taken place during the first month.
now that we have performed some cleaning, will we proceed to perform a train-test split.
flights_split <- initial_split(flights1)
flights_train <- training(flights_split)
flights_test <- testing(flights_split)
we will use the training data set for visual exploratory data analysis to reinforce the idea that we don’t touch the testing data set.
We can look at many things with this data set. What we want to look at is how any of the variables relate to delay.
flights1 %>%
count(hour, delay) %>%
ggplot(aes(hour, delay, fill = n)) +
geom_raster()
We see a varied amount of flights throughout the day. This makes sense, no one wants to leave early or late from the airport.
flights1 %>%
count(minute, delay) %>%
ggplot(aes(minute, delay, fill = n)) +
geom_raster()
We see some strong artifacts in the time of scheduled departure. Most flights leave on a multiple of 5 which we confirm below.
flights1 %>%
count(minute, sort = TRUE)
By combining hour and minute we can look at how much the different flights have departure delays. There are some really long delays in here!
flights1 %>%
mutate(time = hour * 60 + minute) %>%
ggplot(aes(time, dep_delay)) +
geom_point()
If we color the points by delay we see that it appears that most of the delayed arrivals happen because of a delayed departure.
flights1 %>%
mutate(time = hour * 60 + minute) %>%
ggplot(aes(time, dep_delay, color = delay)) +
geom_point(alpha = 0.2)
Let’s begin with a logistic model. We will look at how dep_delay and distance affects delay.
Our first step is to establish which model(s) we want to try on the data.
For now, this is just a logistic model.
To establish the model, we need to determine which R package it comes from (the “engine”) and whether we are doing regression or classification.
(These functions come from the tidymodels package that we loaded in the setup chunk.)
lr_spec <- logistic_reg() %>%
set_engine("glm") %>%
set_mode("classification")
Next, we will fit the model to our data:
lr_fit <- lr_spec %>%
fit(delay ~ dep_delay + distance, data = flights_train)
Let’s check out the output of this model fit:
lr_fit %>%
extract_fit_engine() %>%
summary()
the coefficients are shown as log-odds terms. We could also get this information using tidy()
tidy(lr_fit)
setting exponentiate = TRUE, gives us the odds instead of log-odds.
tidy(lr_fit, exponentiate = TRUE)
We can also take a look at how well the model is doing. By using augment() we can generate predictions, and conf_mat() and autoplot() let us create a confusion matrix and visualize it.
augment(lr_fit, flights_train) %>%
conf_mat(truth = delay, estimate = .pred_class) %>%
autoplot(type = "heatmap")
augment(lr_fit, flights_train) %>%
accuracy(truth = delay, estimate = .pred_class)
Experiment with using some of the other predictors in your model. Are the answers surprising? Evaluate your models with conf_mat() and accuracy().
Once you have a model you like, predict on the test data set and calculate the performance metric. Compare to the performance metrics you got for the training data set.