BaşlayınÜcretsiz Başlayın

Modeling with categorical inputs

For this exercise, you will fit a linear model to the flowers data, to predict Flowers as a function of Time and Intensity.

The model formula fmla that you created in the previous exercise is still available, as is the model matrix mmat.

Bu egzersiz

Supervised Learning in R: Regression

kursunun bir parçasıdır
Kursu Görüntüle

Egzersiz talimatları

  • Use fmla and lm to train a linear model that predicts Flowers from Intensity and Time. Assign the model to the variable flower_model.
  • Use summary() to remind yourself of the structure of mmat.
  • Use summary() to examine the flower_model. Do the variables match what you saw in mmat?
  • Use flower_model to predict the number of flowers. Add the predictions to flowers as the column predictions.
  • Fill in the blanks to plot predictions vs. actual flowers (predictions on the x-axis).

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

# flowers is available
str(flowers)

# fmla is available
fmla

# Fit a model to predict Flowers from Intensity and Time : flower_model
flower_model <- ___

# Use summary on mmat to remind yourself of its structure
___

# Use summary to examine flower_model 
___

# Predict the number of flowers on each plant
flowers$predictions <- ___

# Plot predictions vs actual flowers (predictions on x-axis)
ggplot(___, aes(x = ___, y = ___)) + 
  geom_point() +
  geom_abline(color = "blue")
Kodu Düzenle ve Çalıştır