Data Science-FREE Data Science 1 Flashcards
6 cards from real Data Science practice questions. Tap to flip, then mark Knew It or Still Learning — missed cards come back until you master them.
Read the first 6 Data Science-FREE Data Science 1 flashcards as text
Which measure of central tendency is most resistant to the influence of outliers in a dataset?
Answer: Median
The median is the middle value when data is sorted and is unaffected by extreme values, making it robust to outliers. The mean, by contrast, shifts significantly when outliers are present.
In the context of machine learning, what is the primary purpose of a validation set?
Answer: To tune hyperparameters and select the best model configuration
A validation set is used during development to tune hyperparameters and compare different model configurations. The test set (not the validation set) is reserved for final unbiased evaluation.
Which probability distribution is commonly used to model the number of events occurring within a fixed time interval when events happen independently?
Answer: Poisson distribution
The Poisson distribution models the count of independent events in a fixed interval of time or space, given a known average rate. It is widely used for event-count data such as customer arrivals or server requests per minute.
What is the purpose of the 'learning rate' hyperparameter in gradient descent optimization?
Answer: It controls how much the model's weights are updated at each step
The learning rate scales the gradient update applied to the model's weights at each iteration. A rate too high causes divergence, while a rate too low leads to very slow convergence.
Which of the following best describes the concept of 'feature engineering'?
Answer: Transforming or creating input variables to improve model performance
Feature engineering involves creating new input variables or transforming existing ones — such as encoding categoricals, scaling numerics, or combining columns — to provide the model with more informative signals.
In a linear regression model, what does the R-squared (R²) value indicate?
Answer: The proportion of variance in the target variable explained by the model
R² ranges from 0 to 1 and represents the fraction of the dependent variable's variance that is explained by the independent variables. An R² of 0.85 means the model accounts for 85% of the observed variability.