Page 107 - Data Science Algorithms in a Week

Random Forest

In the previous chapter, decision trees were not able to classify the feature (Cold, None).
So, this time, we would like to find, using the random forest algorithm, whether Jane would
go shopping if the outside temperature was cold and there was no rain.

Analysis:
To perform the analysis with the random forest algorithm we use the implemented
program.

Input:
We put the data from the table into the 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 larger number of the trees that we used in the previous examples
and explanations to get more accurate results. We want to construct a random forest with 20
trees with the output of the low verbosity - level 0. Thus, we execute in a terminal:

$ python random_forest.py shopping.csv 20 0
***Classification***
Feature: ['Cold', 'None', '?']
Tree 0 votes for the class: Yes
Tree 1 votes for the class: No
Tree 2 votes for the class: No
Tree 3 votes for the class: No
Tree 4 votes for the class: No
Tree 5 votes for the class: Yes
Tree 6 votes for the class: Yes
Tree 7 votes for the class: Yes
Tree 8 votes for the class: No
Tree 9 votes for the class: Yes
Tree 10 votes for the class: Yes
Tree 11 votes for the class: Yes
Tree 12 votes for the class: Yes
Tree 13 votes for the class: Yes
Tree 14 votes for the class: Yes

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