A Random Forest makes predictions by aggregating the outputs of multiple decision trees. For classification, each tree votes for a class and the final prediction is the class with the most votes (majority vote).
Given the predictions from T decision trees for N samples, compute the majority vote for each sample. Break ties by choosing the smallest class label.
For each sample, count votes from all trees
Select the class with the highest vote count
If multiple classes are tied, pick the smallest class label
Return one integer class label for each sample.
Input: predictions = [[0, 1, 0], [0, 1, 1], [0, 0, 0]]
Output: [0, 1, 0]
Explanation: Votes are counted column by column across the three trees.
Input: predictions = [[0, 1], [1, 0]]
Output: [0, 0]
Build a vote-count dictionary for one sample column at a time.
Find the largest count, then select the smallest label having that count.
Accepts: array
A Random Forest makes predictions by aggregating the outputs of multiple decision trees. For classification, each tree votes for a class and the final prediction is the class with the most votes (majority vote).
Given the predictions from T decision trees for N samples, compute the majority vote for each sample. Break ties by choosing the smallest class label.
For each sample, count votes from all trees
Select the class with the highest vote count
If multiple classes are tied, pick the smallest class label
Return one integer class label for each sample.
Input: predictions = [[0, 1, 0], [0, 1, 1], [0, 0, 0]]
Output: [0, 1, 0]
Explanation: Votes are counted column by column across the three trees.
Input: predictions = [[0, 1], [1, 0]]
Output: [0, 0]
Build a vote-count dictionary for one sample column at a time.
Find the largest count, then select the smallest label having that count.
Accepts: array