We take the problem from the previous chapter. We have the following data relating to the shopping preferences of our friend, Jane:
Temperature | Rain | Shopping |
Cold | None | Yes |
Warm | None | No |
Cold | Strong | Yes |
Cold | None | No |
Warm | Strong | No |
Warm | None | Yes |
Cold | None | ? |
In the previous chapter, decision trees were not able to classify the feature (Cold, None)
. So, this time, we would like to establish whether Jane would go shopping if the temperature was cold and there was no rain using the random forest algorithm.
To perform an analysis using the random forest algorithm, we use the program implemented.
Input:
We insert the data from the table into the following CSV file:
# source_code/4/shopping.csv
Temperature,Rain,Shopping
Cold,None,Yes
Warm,None,No
Cold,Strong,Yes
Cold,None,No
Warm,Strong,No
Warm,None,Yes
Cold,None,?
Output:
We want to use a slightly higher number of trees than we used in the previous examples...