Machine learning alll quizzes and solutions PDF

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Machine Learning QuizzesQuiz 1Which variables we need to assign the value before we train the model?a. evaluation metricb. model namec. model stated. hyperparameterWhich of the following reason may not cause the bad model performance?a. There are insufficient training datab. The training data contai...

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Machine Learning Quizzes Quiz 1 Which variables we need to assign the value before we train the model?! a. evaluation metric! b. model name! c. model state! d. hyperparameter

Which of the following reason may not cause the bad model performance?! a. There are insufficient training data! b. The training data contains many outliers and noises! c. The training data has irrelevant features! d. The training data is not statistically different from the test data

Regression is distinguished from classification by:! Select one:! a. hypothesis testing! b. numerical target variable c. class probability estimation! d. numerical attributes!

What would be the best possible model to characterize the following task?! Are there any interesting natural#groupings of my customers?! Select one:! a. Reinforcement learning task! b. Unsupervised learning c. Classification task! 1

d. Regression task!

What is the fundamental objective of machine learning task?! Select one:! a. provide business insights with algorithms! b. generalize to predict unseen data beyond the data used to train models c. find hidden patterns in data! d. fortune telling!

What would be the best possible model to characterize the following task?! Which customers will leave within#90 days of their current contract#expiration?! Select one:! a. Regression task! b. Unsupervised learning! c. Classification task d. Reinforcement learning task!

What is NOT the possible solutions when the model underfits?! Select one:! a. Selecting a more powerful model! b. Feeding better features! c. Constraining a model to make it simpler d. Reducing the constraints of the model!

Which of the following statement is not TRUE for unsupervised learning?! a. Unsupervised learning can be used for regression task b. Unsupervised learning does not require the data with labels.! c. Unsupervised learning can be used for anomaly detection task! 2

d. Unsupervised learning can be used for clustering task!

Which of these organizations would have the most challenge in applying supervised#predictive modeling?! Select one:! a. A grocery store that is trying to identify which of its loyalty-card-carrying customers will#spend more than $100 next month.! b. A city government that is trying to predict which neighborhoods will see the most new#businesses open up next quarter.! c. A business school that wants to start a new Master’s degree program in Business"Analytics, and would like to estimate the likely number of applicants d. An online marketing company that wants to estimate the number of clicks that the ads it#serves will receive when shown to a particular population!

What would be the best possible model to characterize the following task?! How many cell phone minutes will#each customer use next month?! Select one:! a. Regression task! b. Classification task! c. Reinforcement learning task! d. Unsupervised learning!

Quiz 2 Which of the following evaluation metric is NOT used for regression tasks?! Select one:! a. mean percentage error! b. adjusted R-squared! c. Area under curve d. Mean absolute error!

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Imagine a linear model with two strongly correlated features; that is, these two features are nearly identical copies of one another but one feature contains a small amount of random noise. If we train this model with L2regularization, what will happen to the weights for these two features?! Select one:! a. None of these! b. One feature will have a large weight; the other will have a weight of#exactly#0.0.! c. One feature will have a large weight; the other will have a weight of#almost#0.0.! d. Both features will have roughly equal, moderate weights

What is the recall of this classifier? Round your answer to 2 decimal places! Select one:! a. 0.81! b. 0.56! c. 0.99 (Recall = 5600 /(5600 + 40) = 0.99) d. 0.75!

Which of the following metric would indicate larger error when it decreases?! Select one:! a. mean absolute error! b. adjusted R-squared c. mean squared error! 4

d. mean percentage error!

Consider two models—A and B—that each evaluate the same dataset. Which one of the following statements is true?! Select one:! a. If Model A has better precision than model B, then model A is better.! b. If model A has better recall than model B, then model A is better.! c. None of these! d. If model A has better precision and better recall than model B, then model A is probably better.

More complex models! Select one:! a. are easier to train than simpler models! b. are very interpretable! c. have better predictive performance! d. tend to overfit more

Suppose a binary classifier produced the following confusion matrix!

! What is the accuracy of this classifier? Round your answer to 2 decimal places! Select one:! a. 0.99! 5

b. 0.56! c. 0.81 (Accuracy: (5600+2460)/(5600+2460+1900+40)=0.81) d. 0.75!

Consider a classification model that separates email into two categories: "spam" or "not spam." If you raise the classification threshold, what will happen to recall?! Select one:! a. Always increase.! b. Always stay constant.! c. Always decrease or stay the same. d. None of these!

Consider a classification model that separates email into two categories: "spam" or "not spam." If you raise the classification threshold, what will happen to precision?! Select one:! a. Definitely decrease! b. Probably increase. c. Definitely increase.! d. Probably decrease!

Suppose a binary classifier produced the following confusion matrix!

! What is the precision of this classifier? Round your answer to 2 decimal places! Select one:! a. 0.56! 6

b. 0.99! c. 0.75 (Precision: 5600/(5600 + 1900)= 0.75) d. 0.81!

Quiz 3 Which of the following algorithm is not an example of an ensemble method?! Select one:! a. Random Forest! b. Adaboost! c. Bagging! d. Decision Tree

A _________ is a decision support tool that uses a tree-like graph or model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility.! Select one:! a. Decision Tree b. Logistic Regression! c. Random Forest! d. Bagging!

In ensemble learning, you aggregate the predictions for weak learners, so that an ensemble of these models will give a better prediction than prediction of individual models.! Which of the following statements is / are true for weak learners used in ensemble model?! 1. They don’t usually overfit.! 2. They have high bias, so they cannot solve complex learning problems! 3. They usually overfit.!

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Select one:! a. 2 and 3! b. 1 and 2 c. 1 and 3! d. Only 1!

Which of the following is not true about logistic regression! Select one:! a. Logistic regression takes a categorical target variable in training data! b. Logistic regression can be used to predict the probability of membership in a certain class! c. Logistic regression requires numeric attributes and categorical attributes should be converted to numeric attributes d. A logistic regression represents the odds of class membership as a linear function of the attributes!

Which of the following algorithm would you take into the consideration in your final model building on the basis of performance?! Suppose you have given the following graph which shows the ROC curve for two different classification algorithms such as Random Forest (Red) and Logistic Regression (Blue)!

! Select one:! a. Both of the above! b. Logistic Regression! 8

c. Random Forest d. None of these!

Which of the following is/are true about bagging trees?! 1. In bagging trees, individual trees are independent of each other! 2. Bagging is the method for improving the performance by aggregating the results of weak learners! Select one:! a. 2! b. 1! c. 1 and 2 d. None of these!

Below are the 8 actual values of target variable in the train set.! [0,0,0,1,1,1,1,1]! What is the entropy of the target variable?#! Select one:! a."-(5/8 log(5/8) + 3/8 log(3/8)) b. 5/8 log(3/8) – 3/8 log(5/8)! c. 3/8 log(5/8) + 5/8 log(3/8)! d. 5/8 log(5/8) + 3/8 log(3/8)!

Imagine, you are solving a classification problems with highly imbalanced class. The majority class is observed 99% of times in the training data.! Your model has 99% accuracy after taking the predictions on test data. Which of the following is true in such a case?!

1. Accuracy metric is not a good idea for imbalanced class problems.! 2. Accuracy metric is a good idea for imbalanced class problems.! 9

3. Precision and recall metrics are good for imbalanced class problems.! 4. Precision and recall metrics aren’t good for imbalanced class problems.! Select one:! a. 1 and 4! b. 2 and 4! c. 2 and 3! d. 1 and 3

In the figure, X1 and X2 are the two features and the data point is represented by dots (-1 is negative class and +1 is a positive class). And you first split the data based on feature X1(say splitting point is x11) which is shown in the figure using vertical line. Every value less than x11 will be predicted as positive class and greater than x will be predicted as negative class.!

! How many data points are misclassified in above image?! Select one:! a. 3!

b. 4! 10

c. 1 d. 2!

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?! 1. Individual tree is built on a subset of the features! 2. Individual tree is built on all the features! 3. Individual tree is built on a subset of observations! 4. Individual tree is built on full set of observations! Select one:! a. 2 and 4! b. 1 and 4! c. 2 and 3! d. 1 and 3

Quiz 4 When you use the boosting algorithm you always consider the weak learners. Which of the following is the main reason for having weak learners?! 1. To prevent overfitting! 2. To prevent under fitting! Select one:! a. 2! b. 1 and 2! c. None of these! d. 1 Which of the following is/are true about boosting trees?! 1. In boosting trees, individual weak learners are independent of each other!

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2. It is the method for improving the performance by aggregating the results of weak learners! Select one:! a. 2 b. both are correct! c. 1! d. None of these!

Which of the following algorithm doesn’t uses learning Rate as of one of its hyperparameter?! 1. Gradient Boosting! 2. Decision Trees! 3. AdaBoost! 4. Random Forest! Select one:! a. 2 and 4 b. 1 and 4! c. 1 and 3! d. 2 and 3!

Which of the following statement about scikit-learn pipeline is NOT True?! a. You just need to call the transform and predict method when applying the pipeline on test data! b. Features that have been reduced with dimensionality can be concatenated with original features to form the feature matrix! c. You can apply preprocess operations on different types of features in parallel!

d. A pipeline can only include estimators that contain fit and predict method

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Which of the following is/are true about Random Forest and Gradient Boosting ensemble methods?! 1. Both methods can be used for classification task! 2. Random Forest is use for classification whereas Gradient Boosting is use for regression task! 3. Random Forest is use for regression whereas Gradient Boosting is use for Classification task! 4. Both methods can be used for regression task! Select one:! a. 1! b. 4! c. 3! d. 1 and 4 e. 2!

Which of the following algorithm are not an example of ensemble learning algorithm?! Select one:! a. Gradient Boosting! b. Adaboost! c. Random Forest! d. Decision Trees

Which of the following is true about the Gradient Boosting trees?! 1. In each stage, introduce a new regression tree to compensate the shortcomings of existing model! 2. We can use gradient decent method for minimize the loss function! Select one:! a. 1 and 2

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b. 2! c. 1! d. None of these!

Consider the hyperparameter “number of trees” and arrange the options in terms of time taken by each hyperparameter for building the Gradient Boosting model?! 1. Number of trees = 100! 2. Number of trees = 500! 3. Number of trees = 1000! Note: remaining hyperparameters are same! Select one:! a. 1=2...