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.

What difficulty level is this interview question?

This is a medium difficulty Machine Learning question, commonly asked during Technical Screen rounds at LinkedIn.

What role is this question designed for?

This question is commonly asked for Machine Learning Engineer candidates at LinkedIn during technical interviews.

Explain Core ML Fundamentals | LinkedIn Interview Question

Quick Overview

This question evaluates foundational machine learning competencies including binary classification and probabilistic model interpretation (logistic regression), classification loss functions, optimization methods (batch vs stochastic gradient descent), regularization and bias–variance trade-offs, and decision tree behavior.

Answer the following machine learning fundamentals questions:

Logistic Regression
- Why is logistic regression suitable for binary classification?
- Why can its output be interpreted as a probability?
Loss Function
- What loss function is commonly used for logistic regression?
- Why is that loss function appropriate?
Gradient Descent
- Compare batch gradient descent and stochastic gradient descent.
- What are the main differences, advantages, and disadvantages of each?
- Is a larger batch size always better? Why or why not?
- In your explanation, address compute efficiency, convergence behavior, gradient noise, and generalization.
Overfitting and Underfitting
- What is overfitting? What is underfitting?
- How can you tell which one is happening?
- How can overfitting be reduced?
- Explain the difference between L1 and L2 regularization, including when each is useful and how they affect sparsity and coefficient shrinkage.
Decision Trees
- In a classification tree, must the output of a leaf node always be exactly 0 or 1?
- Compare two trees:
  - Model A: each leaf contains only one training sample.
  - Model B: each leaf contains multiple training samples.
- Which model is more likely to overfit, and why?

Explain Core ML Fundamentals

Quick Overview

Solution

Comments (0)