Data Science Flashcards

Which of the following is a key assumption of linear regression?

Answer: Residuals are normally distributed and homoscedastic

Linear regression assumes that residuals have constant variance (homoscedasticity) and are normally distributed.

What problem does the SMOTE technique address in machine learning?

Answer: Class imbalance in the training dataset

SMOTE (Synthetic Minority Oversampling Technique) generates synthetic samples for the minority class to balance the dataset.

In the context of unsupervised learning, what does the silhouette score measure?

Answer: How well each data point fits its own cluster versus neighboring clusters

The silhouette score ranges from -1 to 1, where higher values indicate well-separated, cohesive clusters.

What is the primary advantage of using gradient boosting over random forests?

Answer: It sequentially corrects errors from previous trees, often achieving lower bias

Gradient boosting fits each new tree to the residuals of the ensemble so far, reducing bias iteratively.

Which metric is best suited for evaluating a regression model's performance?

Answer: Root Mean Squared Error (RMSE)

RMSE measures the average magnitude of prediction errors in the same units as the target variable.

What is the purpose of the train-validation-test split in machine learning?

Answer: To use training for learning, validation for hyperparameter tuning, and test for final unbiased evaluation

Keeping a held-out test set ensures the final performance estimate is unbiased by any model selection decisions.

In a neural network, what is the vanishing gradient problem?

Answer: Gradients shrink exponentially through layers, slowing learning in early layers

When gradients approach zero during backpropagation, early layers learn very slowly or not at all.