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# A Foundation Program in Data Science Quiz Answers – Grey Campus

• linear, numeric
• linear, binary
• nonlinear, numeric
• nonlinear, binary
• PCA
• Decision Tree
• Linear Regression
• Naive Bayesian
• Least Square Error
• Maximum Likelihood
• Logarithmic Loss
• Both A and B
• The polynomial degree
• Whether we learn the weights by matrix inversion or gradient descent
• The use of a constant-term
• None of the above
• Mean of residuals is always zero
• Mean of residuals is always less than zero
• Mean of residuals is always greater than zero
• There is no such rule for residuals.
• Linear Regression with varying error terms
• Linear Regression with constant error terms
• Linear Regression with zero error terms
• None of these
• Scatter plot
• Bar chart
• Histograms
• None of these
• Ridge regression uses subset selection of features
• Lasso regression uses subset selection of features
• Both use subset selection of features
• None of above
• The relationship is symmetric between x and y in both.
• The relationship is not symmetric between x and y in both.
• The relationship is not symmetric between x and y in case of correlation but in case of regression it is symmetric.
• The relationship is symmetric between x and y in case of correlation but in case of regression it is not symmetric.
• Ridge regression
• Lasso
• Both Ridge and Lasso
• None of both
• The polynomial degree
• Whether we learn the weights by matrix inversion or gradient descent
• The use of a constant-term
• None of the above
• You will always have test error zero
• You can not have test error zero
• None of the above
• Both A and B
• Correlation coefficient = 0.9
• The p-value for the null hypothesis Beta coefficient =0 is 0.0001
• The t-statistic for the null hypothesis Beta coefficient=0 is 30
• None of these
• Random Forest
• Extra Trees
• Decision Trees
• 1 and 2
• 2 and 3
• 1 and 3
• 1,2 and 3
• Measure performance over training data
• Measure performance over validation data
• Both of these
• None of these
• Classifiers which form a tree with each attribute at one level.
• Classifiers which perform series of condition checking with one attribute at a time.
• Both the options given above.
• None of the above
• By pruning the longer rules
• By creating new rules
• Both By pruning the longer rules’ and ‘ By creating new rules’
• None of the option
• In pre-pruning a tree is ‘pruned’ by halting its construction early.
• A pruning set of class labeled tuples is used to estimate cost complexity.
• The best pruned tree is the one that minimizes the number of encoding bits.
• All of the above
• CART
• ID3
• Bayesian Classifier
• Random Forest