Sample Questions
Question 1
Easy
In machine learning, overfitting most likely occurs when:
Solution
B is correct.
Overfitting occurs when a model is too complex relative to the amount of training data, causing it to learn patterns specific to the training set (including random noise) rather than the true underlying relationship. This results in high accuracy on training data but poor generalization to new data.
Overfitting occurs when a model is too complex relative to the amount of training data, causing it to learn patterns specific to the training set (including random noise) rather than the true underlying relationship. This results in high accuracy on training data but poor generalization to new data.
Question 2
Medium
In hypothesis testing, a Type I error occurs when:
Solution
B is correct.
A Type I error is the rejection of a true null hypothesis. The probability of committing a Type I error is equal to the significance level (alpha) of the test.
A Type I error is the rejection of a true null hypothesis. The probability of committing a Type I error is equal to the significance level (alpha) of the test.
Question 3
Hard
The excess kurtosis of a normal distribution is:
Solution
B is correct.
A normal distribution has a kurtosis of 3. Excess kurtosis is defined as kurtosis minus 3, so for a normal distribution, excess kurtosis = 3 - 3 = 0. A distribution with excess kurtosis greater than 0 is leptokurtic (fatter tails), while one with excess kurtosis less than 0 is platykurtic (thinner tails).
A normal distribution has a kurtosis of 3. Excess kurtosis is defined as kurtosis minus 3, so for a normal distribution, excess kurtosis = 3 - 3 = 0. A distribution with excess kurtosis greater than 0 is leptokurtic (fatter tails), while one with excess kurtosis less than 0 is platykurtic (thinner tails).
