Basic Machine Learning algorithms

The coefficients of the least-squares regression line are determined by minimizing the sum of the squares of the

• x-coordinate

• y-coordinate

• residuals

A residual is computed as

• a y‐coordinate from the data minus a y‐coordinate predicted by the line.

• a y‐coordinate predicted by the line minus a y‐coordinate from the data.

• an x‐coordinate from the data minus an x‐coordinate predicted by the line.

• an x‐coordinate predicted by the line minus an x‐coordinate from the data.

A geometric interpretation of a residual is the

• perpendicular distance from a data point to the regression line.

• horizontal distance from a data point to the regression line.

• vertical distance from a data point to the regression line.

Which of the following statement is TRUE about the Naive Bayes classifier?

• Bayes classifier works on the Bayes theorem of probability.

• Bayes classifier is an unsupervised learning algorithm.

• Bayes classifier is based on prior probability.

• It assumes that all features are statistically dependent on each other.

Which of the following methods do we use to best fit the data in Logistic Regression?

• Least Square Error

• Maximum Likelihood

• Euclidean distance

• Posterior probability

Which of the following evaluation metrics can be applied in case of logistic regression output to compare with target? (check all that applies)

• Sensitivity

• Accuracy

• Mean square error

• R2

Which of the following comparison(s) are true about PCA and LDA?

1) Both LDA and PCA are linear transformation techniques

2) LDA is unsupervised whereas PCA is supervised

3) PCA maximize the variance of the data, whereas LDA maximize the separation between different classes,

• 1 and 2

• 1 and 3

• 2 and 3

• 1,2 and 3

Imagine, you are given the following scatterplot between height and weight.

Select the angle which will capture maximum variability along a single axis?

• ~ 0 degree

• ~ 45 degree

• ~ 60 degree

• ~ 90 degree

Which of the following option is true?

• PCA explicitly attempts to model the separability between the classes of data. LDA on the other hand does not take into account any difference in class, but the variability of the predictors.

• Both attempt to model the separability between the classes of data.

• LDA explicitly attempts to model the separability between the classes of data. PCA on the other hand does not take into account any difference in class, but the variability of the predictors.

• Both don’t attempt to model the separability between the classes of data.

In Random forest you can generate hundreds of trees (say T1, T2 …..Tn) and then aggregate the results of these tree. Which of the following is true about individual(Tk) tree in Random Forest? (you can choose more than one)

• Individual tree is built on a subset of the features

• Individual tree is built on all the features

• Individual tree is built on a subset of observations

• Individual tree is built on full set of observations