Handle Missing Values and Choose ML Algorithms Wisely

Q: Handle Missing Values and Choose ML Algorithms Wisely

This question evaluates a data scientist's competencies in data preprocessing (handling missing values), algorithm selection and justification, model interpretation (contrasting Random Forests with linear regression), and understanding of model generalization (overfitting and underfitting).

Q: How do I approach Machine Learning interview questions?

Machine Learning questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master machine learning interviews.

Question

ML Interview: Core Modeling Concepts

Context: Technical phone screen for a Data Scientist role. Assume primarily tabular datasets; address both classification and regression where relevant.

Questions

(a) How would you handle missing values before model training, and why?

(b) Given a business scenario, how would you choose an appropriate ML algorithm and justify your choice?

(c) Explain Random Forests in lay terms and contrast them with linear regression.

(d) Define overfitting and underfitting, and describe methods to detect and mitigate each.

Handle Missing Values and Choose ML Algorithms Wisely

ML Interview: Core Modeling Concepts

Questions

Solution

Comments (0)

Handle Missing Values and Choose ML Algorithms Wisely

Overview

ML Interview: Core Modeling Concepts

Questions

Solution

Comments (0)