Answer-first summary for fast verification
Answer: Decision Trees, K-Nearest Neighbors
Nonparametric methods are ideal for datasets with unknown distributions as they do not assume any specific form for the function that maps inputs to outputs. Decision Trees (A) and K-Nearest Neighbors (B) are both nonparametric because they make no assumptions about the underlying data distribution. Decision Trees are particularly interpretable, making them suitable for scenarios where understanding the model's decision process is crucial. K-Nearest Neighbors, while less interpretable, is simple and effective for classification tasks. Logistic Regression (C) and Simple Neural Networks (D) are parametric, as they assume a specific form for the underlying function. Support Vector Machines with a linear kernel (E) are also parametric, as they assume a linear decision boundary. Therefore, the correct choices are A (Decision Trees) and B (K-Nearest Neighbors).
Author: LeetQuiz Editorial Team
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In your role as a Data Scientist at a tech startup, you're tasked with developing a predictive model for customer churn. Your team is considering using nonparametric Machine Learning algorithms due to their flexibility in handling the unknown distribution of your dataset. However, there's a debate on which algorithms truly qualify as nonparametric. Given the constraints of limited computational resources and the need for interpretability, which of the following algorithms are considered nonparametric and would be most suitable for this scenario? (Choose two options)
A
Decision Trees
B
K-Nearest Neighbors
C
Logistic Regression
D
Simple Neural Networks
E
Support Vector Machines with a linear kernel
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