Knowledge Flashcards
7 cards from real DSE practice questions. Tap to flip, then mark Knew It or Still Learning โ missed cards come back until you master them.
Read the first 7 Knowledge flashcards as text
Which probability distribution is most appropriate for modeling the number of events occurring in a fixed time interval?
Answer: Poisson distribution
The Poisson distribution models the count of independent events occurring in a fixed interval of time or space.
What does the term 'bias-variance tradeoff' describe in machine learning?
Answer: The tension between underfitting (high bias) and overfitting (high variance)
Bias-variance tradeoff refers to the tension between a model's error due to oversimplification (bias) and its sensitivity to training data fluctuations (variance).
In SQL, what does a LEFT JOIN return?
Answer: All rows from the left table and matching rows from the right
A LEFT JOIN returns all rows from the left table and the matched rows from the right table; unmatched right-table rows appear as NULL.
What is the purpose of cross-validation in model evaluation?
Answer: To estimate model performance on unseen data using multiple train/test splits
Cross-validation repeatedly splits data into training and validation sets to produce a more reliable estimate of generalization performance.
Which measure of central tendency is most resistant to outliers?
Answer: Median
The median is resistant to outliers because it depends only on the middle value(s), not the magnitude of extreme values.
What does PCA (Principal Component Analysis) primarily accomplish?
Answer: Reduces dimensionality by projecting data onto directions of maximum variance
PCA transforms features into a smaller set of uncorrelated principal components that capture the most variance in the data.
In the context of hypothesis testing, what does a p-value represent?
Answer: The probability of observing results at least as extreme as those seen, assuming the null hypothesis is true
The p-value is the probability of obtaining a test statistic as extreme or more extreme than observed, under the assumption that the null hypothesis is true.