Problem1: Let's take the example of playing chess from Chapter 2, Naive Bayes. How would you classify a (Warm,Strong,Spring,?)
data sample according to the random forest algorithm?
Temperature | Wind | Season | Play |
Cold | Strong | Winter | No |
Warm | Strong | Autumn | No |
Warm | None | Summer | Yes |
Hot | None | Spring | No |
Hot | Breeze | Autumn | Yes |
Warm | Breeze | Spring | Yes |
Cold | Breeze | Winter | No |
Cold | None | Spring | Yes |
Hot | Strong | Summer | Yes |
Warm | None | Autumn | Yes |
Warm | Strong | Spring | ? |
Problem 2: Would it be a good idea to use only one tree and a random forest? Justify your answer.
Problem 3: Can cross-validation improve the results of the classification by the random forest? Justify your answer.
Problem 1: We run the program to construct the random forest and classify the feature (Warm, Strong, Spring
).
Input:
source_code/4/chess_with_seasons.csv
Temperature,Wind,Season,Play
Cold,Strong,Winter,No
Warm,Strong,Autumn,No
Warm,None,Summer,Yes
Hot,None,Spring,No
Hot,Breeze,Autumn,Yes
Warm,Breeze,Spring,Yes
Cold,Breeze,Winter,No
Cold,None,Spring,Yes
Hot...