Lab 4F
Directions: Follow along with the slides and answer the questions in red font in your journal.
movie
data and split it into two sets:
training
that includes 75% of the data.testing
that includes the remaining 25%.set.seed
.audience_rating
based on critics_rating
for the training
data. Assign this model to movie_linear
.audience_rating
on the y-axis and critics_rating
on the x-axis using your testing
data.add_line
to plot the line of best fit. An alternative function for plotting the line of best fit is add_curve
, which takes the name of the model as an argument.training
data to the plot.critics_rating
that would make obviously poor predictions?
critics_rating
?testing
data and write it down for later.
y = a + bx
y = a + bx + cx
2
y = a + bx + cx
2
+ dx
3
R
, we can use the poly()
function.
audience_rating
from critics_rating
, and assign that model to movie_quad
.2
in the poly()
function?audience_rating
on the y-axis and critics_rating
on the x-axis using your testing
data, andcol
argument is added to the add_curve
functions to help distinguish the two curves.test
MSE?test
data and write it down for later.test
MSE to describe why one model fits better than the other.audience_rating
using a cubic curve (polynomial with degree 3
), and assign this model to movie_cubic
.audience_rating
on the y-axis and critics_rating
on the x-axis using your test
data.training data
to the plot.testing
data?testing data
.