AI AI Engineer: Machine Learning Fundamentals and Algorithms

Question 1

What does the bias-variance tradeoff describe in machine learning?

Accepted Answer

The balance between model complexity and generalization to unseen data

Answer

The bias-variance tradeoff describes how increasing model complexity reduces bias but increases variance, while simpler models have high bias but low variance — the goal is to minimize total error on unseen data.

Question 2

Which regularization technique adds the sum of the absolute values of model weights as a penalty term to the loss function?

Accepted Answer

L1 (Lasso) regularization

Answer

L1 (Lasso) regularization penalizes the sum of absolute values of weights, which encourages sparsity by driving some weights to exactly zero, effectively performing feature selection.

Question 3

What is the primary purpose of k-fold cross-validation?

Accepted Answer

To obtain a more reliable estimate of model performance by using all data for both training and validation

Answer

K-fold cross-validation splits data into k subsets, trains on k-1 folds, and validates on the remaining fold, rotating until all folds are used — giving a robust performance estimate without wasting data.

Question 4

Which evaluation metric is most appropriate when classes are severely imbalanced and the cost of false negatives is high?

Accepted Answer

F1 Score

Answer

The F1 Score is the harmonic mean of precision and recall, making it suitable for imbalanced datasets where accuracy is misleading — it balances the cost of false positives and false negatives.

Question 5

What is the primary advantage of ensemble methods like Random Forest over a single decision tree?

Accepted Answer

They reduce variance by aggregating predictions from multiple models

Answer

Random Forest reduces variance by averaging predictions from many decision trees trained on random subsets of data and features, resulting in better generalization than a single tree which tends to overfit.

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AI AI Engineer: Machine Learning Fundamentals and Algorithms