Question

Name the algorithm which makes predictions based on the outcomes of several decision trees.

• A. Classification algorithm
• B. K-means clustering
• C. Random Forest algorithm
• D. K-nearest neighbour algorithm

Hint:

Random Forest = Multiple decision trees combined for better predictions.

Answer 1

The Correct Option is C

The Random Forest algorithm is an ensemble learning technique that constructs numerous decision trees during the training phase.
It consolidates the outputs from individual decision trees to generate a final prediction, either through averaging for regression tasks or voting for classification tasks.
This method enhances accuracy and mitigates the risk of overfitting when contrasted with the utilization of a solitary decision tree.
Option (A), Classification algorithm, represents a broad category and is not specific to decision trees.
Option (B), K-means clustering, is an unsupervised learning algorithm employed for data partitioning, distinct from decision trees.
Option (D), K-nearest neighbour algorithm, makes predictions based on proximate data points and does not employ decision trees.
Therefore, the Random Forest algorithm is the accurate response.