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 Meta.

What role is this question designed for?

This question is commonly asked for Software Engineer candidates at Meta during technical interviews.

Explain key ML metrics and techniques | Meta Interview Question

Q: Explain key ML metrics and techniques

This question evaluates understanding of core Machine Learning concepts including classification evaluation metrics, ensemble methods (bagging vs. boosting), regularization (L1 vs. L2), and two-layer neural network forward computation, testing both model-evaluation and model-building competencies.

You are asked a set of short conceptual machine learning questions.

Confusion matrix and metrics
For a binary classification problem:
- Define the entries of the confusion matrix: true positive (TP), false positive (FP), true negative (TN), and false negative (FN).
- Using TP, FP, TN, FN, write formulas for accuracy, precision, recall, and (optionally) F1-score.
- Briefly explain in words what precision and recall each measure.
Ensemble learning
- What is ensemble learning?
- Why can combining multiple base models into an ensemble improve performance?
- Briefly describe common ways to combine model outputs.
Bagging vs. boosting
Compare bagging and boosting along these dimensions:
- How each method constructs training sets and trains base learners.
- Whether each method primarily reduces bias, variance, or both.
- The main advantages and disadvantages of each.
- Name at least one common algorithm that uses bagging and one that uses boosting.
L1 vs. L2 regularization
Consider a supervised learning model with loss function L(w) over parameters w and a regularization term with strength λ (lambda):
- Write the objective for L1-regularized training and L2-regularized training.
- Explain how L1 and L2 regularization each affect the learned parameters (e.g., sparsity vs. shrinkage).
- Discuss when you might prefer L1 over L2, and vice versa.
Two-layer neural network forward pass
Consider a simple two-layer feedforward neural network: input → hidden layer → output layer.
- Let the input vector be x . The hidden layer uses weight matrix W1 and bias vector b1 with activation function g applied elementwise.
- The output layer uses weight matrix W2 and bias vector b2 with activation function f (e.g., identity, sigmoid, or softmax).
  (a) Write the mathematical expressions for the hidden activations and final output in terms of x , W1 , b1 , W2 , b2 , g , and f .
  (b) Briefly describe how you would carry out a concrete numerical computation of the network output given specific numeric values for these quantities.

You are asked a set of short conceptual machine learning questions.

Confusion matrix and metrics
For a binary classification problem:
- Define the entries of the confusion matrix: true positive (TP), false positive (FP), true negative (TN), and false negative (FN).
- Using TP, FP, TN, FN, write formulas for accuracy, precision, recall, and (optionally) F1-score.
- Briefly explain in words what precision and recall each measure.
Ensemble learning
- What is ensemble learning?
- Why can combining multiple base models into an ensemble improve performance?
- Briefly describe common ways to combine model outputs.
Bagging vs. boosting
Compare bagging and boosting along these dimensions:
- How each method constructs training sets and trains base learners.
- Whether each method primarily reduces bias, variance, or both.
- The main advantages and disadvantages of each.
- Name at least one common algorithm that uses bagging and one that uses boosting.
L1 vs. L2 regularization
Consider a supervised learning model with loss function L(w) over parameters w and a regularization term with strength λ (lambda):
- Write the objective for L1-regularized training and L2-regularized training.
- Explain how L1 and L2 regularization each affect the learned parameters (e.g., sparsity vs. shrinkage).
- Discuss when you might prefer L1 over L2, and vice versa.
Two-layer neural network forward pass
Consider a simple two-layer feedforward neural network: input → hidden layer → output layer.
- Let the input vector be x . The hidden layer uses weight matrix W1 and bias vector b1 with activation function g applied elementwise.
- The output layer uses weight matrix W2 and bias vector b2 with activation function f (e.g., identity, sigmoid, or softmax).
  (a) Write the mathematical expressions for the hidden activations and final output in terms of x , W1 , b1 , W2 , b2 , g , and f .
  (b) Briefly describe how you would carry out a concrete numerical computation of the network output given specific numeric values for these quantities.

Explain key ML metrics and techniques

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Explain key ML metrics and techniques

Quick Overview

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