WorksheetsMachine Learning - Unit 1 - Quiz 1
Total questions: 10
Worksheet time: 6mins
A company wants to predict whether a customer will make a purchase based on their browsing history, time spent on the site, and past purchase behavior. Which type of learning would be most appropriate?
Supervised Learning
Semi Supevised Learning
Reinforment Learning
Unsupervised Learning
A weather forecasting team uses historical temperature data (time vs. temperature) and wants to model the relationship between time and temperature using a polynomial curve. What is the primary objective of using polynomial curve fitting in this context?
To classify temperature data into categories
To find the relationship between time and temperature
To detect anomalies in temperature data
To reduce the dimensionality of the data
What happens if a very high-degree polynomial is used for curve fitting?
It leads to a better generalization on unseen data
It ensures the model is simpler and less complex
It may lead to overfitting, capturing noise rather than the underlying pattern
It will always provide the best fit
What is the sum of the probabilities of all possible outcomes of a discrete random variable?
0
1
Both 0 and 1
Depends on the variable
Can be greater than 1 if there are many outcomes
Bayes’ rule is used for which of the following purposes?
To calculate the probability of the union of two events
To find the probability of the intersection of two independent events
To update the probability of a hypothesis given new evidence
To determine if two events are mutually exclusive
Which of the following statements is correct regarding conditional probability?
Conditional probability is the probability of an event occurring without any given conditions.
It is always equal to the unconditional probability of the event.
It only applies to continuous random variables.
Conditional probability is used when the occurrence of one event affects the likelihood of another.
What is the probability of a continuous random variable taking any exact value?
Always 1
Always 0
only 0 and 1
Equal to the density at that value
Depends on the value
The expected value (mean) of a continuous random variable X with probability density function f(x) is calculated using:
∫ f(x)dx
∫ x f(x)dx
∫ x dx
∫ x^2 f(x)dx
What does the quantile of a continuous random variable represent?
The probability density at a given point
The expected value of the random variable
The sum of all possible values of the random variable
A value below which a specified proportion of observations fall
The variance of a continuous random variable X is minimized when:
All values of X are equal to the mean.
X has a large spread around the mean.
X takes only positive values.
The variance is always zero.
