Quiz on Maximum Likelihood Estimation and Related Concepts

What does Maximum Likelihood Estimation (MLE) aim to estimate?

• The variance of a dataset

• The parameters of a probability distribution

• The mean of a dataset

• The correlation between variables

Which distribution is characterized by mean (μ) and variance (σ²)?

• Binomial distribution

• Normal distribution

• Exponential distribution

• Gamma distribution

What is the main advantage of Bayesian linear regression?

• It does not require any assumptions

• It is always less complex

• It incorporates prior beliefs about parameters

• It requires less data

What is the purpose of the kernel trick in SVM?

• To transform data into a higher-dimensional space

• To reduce the dimensionality of data

• To eliminate outliers from the dataset

• To visualize data in 2D

Which method is used in robust linear regression to reduce the influence of outliers?

• Maximum likelihood estimation

• Ridge regression

• Huber loss

• Mean squared error

What does the Fisher criterion aim to maximize in discriminant functions?

• Overall accuracy

• Between-class variance

• Total variance

• Within-class variance

What is a key feature of the Laplace approximation?

• It is applicable to discrete variables only

• It is used for linear regression only

• It finds a Gaussian approximation to a probability density

• It requires a large sample size

In SVM, what are the points closest to the hyperplane called?

• Support vectors

• Decision boundaries

• Classifiers

• Margins

What does the RBF kernel help to capture in SVM?

• Outlier data points

• Simple linear relationships

• Complex nonlinear relationships

• Linear relationships

What is the main purpose of using a Gaussian prior in ridge regression?

• To increase model complexity

• To mitigate overfitting

• To simplify the model

• To enhance data variability