Machine Learning MCQ Questions: Practice and Test Your Knowledge

A. weights are regularized with the l1 norm

B. the weights have a gaussian prior

C. weights are regularized with the l2 norm

D. the solution algorithm is simpler

A. k-means

B. density-based spatial clustering

C. spectral clustering find clusters

D. all above

A. unsupervised learning algorithm

B. semi-supervised learning algorithm

C. supervised learning algorithm

D. soft margin classifier

A. the attributes are not linearly related.

B. as the value of one attribute increases the value of the second attribute also increases

C. as the value of one attribute decreases the value of the second attribute increases

D. the attributes show a linear relationship

A. F-tests and p-values

B. chi-square

C. ANOVA

D. All above

A. sales forecasting

B. data validation

C. risk management

D. all of the mentioned

A. the output attribute must be cateogrical

B. all attribute values must be categorical

C. all attributes must be numeric

D. attribute values may be either categorical or numeric

A. The polynomial degree

B. Whether we learn the weights by matrix inversion or gradient descent

C. The use of a constant-term

D. None of these

A. $$\frac{{{\text{x}},{\text{y}}}}{{{\text{total number of transactions}}}}$$

B. $$\frac{z}{{{\text{total number of transactions}}}}$$

C. $$\frac{z}{{{\text{x,y}}}}$$

D. $$\frac{{{\text{x}},{\text{y,}}\,{\text{z}}}}{{{\text{total number of transactions}}}}$$

A. a conditional probability

B. an a priori probability

C. a bidirectional probability

D. a posterior probability