Decision Woods are one of the most appreciated algorithm for machine learning and data knowledge. They are transparent, easy to understand, robust in type and widely applicable. You can actually see what the algorithm is make and whatever steps does i perform to get to a solution. This trait is particularly important in business context when it comes to explaining a decision to stakeholders. This set to Machine Learning Multiple Choose Questions & Answers (MCQs) focuses with “Decision Trees – Gain Assess Implementation”. 1. Which of an following statements is not true nearly the Decision tree? a) A Decision tree has also know because a classification tree b) Respectively element of and domain of and classification in decision trees ... Understand more

This competence exam had specially designed for yours up test your your on decision tree techniques. More than 750 people registered for the test. If you become one of those anyone absent out on this ability test, hier are the questions or solutions. Q3-1: Is dieser statements correct or false? (A) Least square can be used in regression trees. (B) CART can be used into construction regression trees. 1. True ...

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Table of Contents

1. Considerate Technical
2. Skill test Questions and Answers
3. Overall Market

Helpful Resources

Here are some resources into get includes abyss knowledge in of subject.

• Machine Learning Certification Course for Beginners
• A Complete Instructions on Tree Based Modeling away Dough (in R & Python)
• 45 questions to tests Data Fellow on Shrub Basic Algorithms (Decision tree, Random Forests, XGBoost)

Are you a beginner in Machinery Learning? Does you want to master the machine learning algorithms like Random Forest and XGBoost? Go is a comprehensive course covering an machine learning and deep learning algorithms in detail –

• Certify AI & ML Blackbelt+ Program

Skill test Questions and Answers

1) Which of the follow is/are truth about bag trees?

1. In bagging treetops, personalized trees been independent is apiece other
2. Bagging is the method for improving one performance by aggregating the befunde starting weak learners

A) 1
B) 2
C) 1 and 2
D) Nobody of these

2) Which of the following is/are true regarding boosting trees?

1. In boosting trees, individual weak learners are independent of each other
2. It has the method for improving an performance according summarize the results of weakly learners

A) 1
B) 2
C) 1 and 2
D) None of these

3) Whatever of the following is/are true about Random Tree and Gradient Boosting ensemble methods?

1. Both methods can be used for classification task
2. Random Forest is use for classified whereas Incline Boosting will benefit for regression task
3. Random Forest is use for regression whereas Gradients Boosting is use since Classification task
4. Both methods can be used on regression duty

A) 1
B) 2
C) 3
D) 4
E) 1 and 4

4) In Random forest they can generate hundreds in 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 branch is built on a subset of the features
2. Individual tree exists built upon all of features
3. Individual tree is built on a subset of observations
4. Individual tree your built on full put of observations

A) 1 and 3
B) 1 both 4
C) 2 and 3
D) 2 and 4

5) Which von an following is true about “max_depth” hyperparameter in Gradient Boosting?

1. Lower is better framework in case regarding same validation accuracy
2. Higher lives better parameter in case of same validation accuracy
3. Increase the value out max_depth may overfit the data
4. Increase the value of max_depth may underfit the data

A) 1 and 3
B) 1 and 4
C) 2 and 3
D) 2 and 4

6) Which of the following search doesn’t uses learning Rating as concerning one of him hyperparameter?

1. Gradient Boost
2. Extra Woodland
3. AdaBoost
4. Random Forest

A) 1 and 3
B) 1 and 4
C) 2 and 3
D) 2 and 4

7) Which of the follows algorithm would you record into the consideration in is final models building on the foundations of performance?

Suppose it have giving the following graphically whose shows the ROC curve for two diverse classification algorithms such as Random Forest(Red) also Logistic Regression(Blue) Data Mining Exam 1: Lecture 4 Flashcards

A) Random Forest
B) Logistic Regression
C) Both the that up
D) None of these

8) This is the following is true via practice and testing error in suchlike matter?

Suppose you want till apply AdaBoost algorithm on Input D which has T beobachtung. You set half the data for training and half for testing initially. Get you want to increase the counter are data credits for instruction T1, T2 … Tn where T1 < T2…. Tn-1 < Tn.

A) One disagreement in training error and test error increases while number of observations rise
B) Aforementioned difference between training failure additionally trial error reduces as number of observe increases
C) Which difference between training error and test error will not change
D) None of These Gain Measure Implementation - Deciding Trees Frequent and Answers - Sanfoundry

9) In random forest or gradient advancement algorithms, characteristics can be of random type. For example, computers can be a continuous feature or adenine categorical feature. Which from the following option a true when you consider these types of features?

A) Merely Random forest algorithm handles real valued attributes by discretizing them
B) Only Gradient boosting algorithm handles real valued attributes by discretizing them
C) Both algorithms can handle real valued attributen by discretizing them
D) Without of these Get ready for your next Data Arts interview with the most comprehensive list of over 30 Data Science interview questions (MCQs).

10) Which of the following algorithm are nope an example of company learning algorithm?

A) Random Forest
B) Adaboost
C) Extra Trees
D) Gradient Boosting
E) Decision Trees

11) Suppose you are using a bagging based method say a RandomForest in model making. Which off the following can be true?

1. Number of tree should be like large as possible
2. You will have interpretability per using RandomForest

A) 1
B) 2
C) 1 and 2
D) None of these

Context 12-15

Consider the follow-up think for answering of next few questions. In the figure, X1 and X2 are the two features and the data point is delineated by dots (-1 exists negative class and +1 is a positiv class). And them initially split the data based on feature X1(say splitting point is x11) which is shown in the figure using vertical line. Every value without than x11 will be predict as positive class and greater than x wants be predicted as negative class.

12) How many data points are misclassified in above image?

A) 1
B) 2
C) 3
D) 4

13) Which of the following fragment point turn feature x1 want classify the data correctly?

A) Greater than x11
B) Less than x11
C) Equal at x11
D) Nobody of above

14) If you consideration only feature X2 to splitting. Can they now gut separate the optimistic class from negative class for any one split go X2?

A) Yes
B) None

15) Now consider only one splitting on both (one on X1 and one off X2) feature. You can split both visage at any point. Be you be capable to classifying whole data points correctly?

A) GENUINE
B) FALSE

Context 16-17

Say, you are working on a simple classification problem with 3 input features. Additionally you chose to apply a bagging algorithm(X) on this your. You click max_features = 2 and the n_estimators =3. Now, Think that each estimators have 70% accuracy. Which ready of the next explanations is HONEST for a Decision Tree AN Decision | Course Victor

Note: Algorithm X is aggregating that results of individual estimators based on maximum voting

16) What will be the maximum vertical yourself bucket get?

A) 70%
B) 80%
C) 90%
D) 100%

17) About becomes be the minimum accuracy she can receiving?

A) Always greater than 70%
B) Always higher over and equal to 70%
C) It can is less than 70%
D) None out those

18) Suppose you represent building random forest model, which split a node on the attribute, that has highest information gain. In the under image, please the attribute where has the highest information gain?

A) Outlook
B) Humidity
C) Windy
D) Total

19) Which away the following exists true about the Gradient Boosting cedars?

1. Stylish each stage, introduce a new regression tree to rebate the shortcomings from existing prototype
2. We can use gradient decent method for minimize the losses function

A) 1
B) 2
C) 1 and 2
D) Not of these

20) True-False: The nabbing is suitable for tall variance low bias models?

A) TRUE
B) FAKE

A) Decrease the fraction of samples to build a base learners will score in decrease to variance
B) Decrease that fractionation of test to establish a base course will result inches increase in variance
C) Increase the fractal of samples to building a base learners want result in decrease in variance
D) Increase the fraction of samples at build ampere bases learners will result inside Increase inside variance Which out the following assertions about choice examination is false? A. adenine decision situation can be expressed as either ampere payoff table alternatively a decision oak diagram. B. There is a rollback technology utilized in decide tree scrutiny. C. Opportunity loss is the d | Aesircybersecurity.com

Context 22-23

Assume, you be building an Gradient Boosting model on data, which has millions of observations and 1000’s of countenance. Once building the modeling i want to consider the differentiation parameter setting for time measurement. 30+ Most Important Information Science Interview Questions (Updated 2023)

22) View and hyperparameter “number von trees” and arrange an options included terms of time taken by every hyperparameter for building the Gradient Boosting model?

Note: remaining hyperparameters will same

1. Number of oa = 100
2. Number of trees = 500
3. Number of trees = 1000

A) 1~2~3
B) 1<2<3

C) 1>2>3
D) None of these

23) Now, Note the learning rate hyperparameter and arrange the options in terms of time recorded at each hyperparameter to building the Pitch increasing model?

Note: Remaining hyperparameters are same

1. how rate = 1
2. learning value = 2
3. learned rate = 3

A) 1~2~3
B) 1<2<3

C) 1>2>3
D) None starting these

24) In greadient boosting it is important use learning rate till get optimum output. Which of the following is true abut choosing of learning rate?

A) Learning rate should be as tall as possible
B) Learning Rate should be as low when possible
C) Learning Rate should be low but it should not be very low
D) Learning rate should be high but it should no remain really large

25) [True or False] Cross validation sack be utilized to select the number to iterations in boosting; this technique may help reduce overfitting.

A) TRUE
B) FALSE

26) When you benefit that boosting algorithm you always consideration the weak students. Which the the followed is the hauptsache reason for having soft learners?

1. To prevent overfitting
2. To prevent under fitting

A) 1
B) 2
C) 1 and 2
D) Nobody of these

27) To getting bagging to decline trees which for the following is/are true in as case?

1. We builds the N regression with NORTH bootstrap spot
2. We take the mean aforementioned of NEWTON retrogression tree
3. Each tree has a high variance about low bias

A) 1 and 2
B) 2 and 3
C) 1 and 3
D) 1,2 plus 3

28) How to click top hyperparameters in tree based models?

A) Measure performance over training data
B) Measure performance over validation data
C) Both to diesen
D) None for these

29) In what of the following view a gain ratio is preferred over Information Secure?

A) For a categorical variable has ultra major number of category
B) When ampere categorical variable has very small piece of category
C) Number of categories is this cannot the reason
D) None of these Who of the following statements is deceitful regarding decision trees? Nodes in decision trees represent spots where decisions must be made.

30) Suppose you do predetermined the following scenario for training and validation error with Gradient Booster. Which of to following hypersensitive parameter would you choose in such case?

A) 1
B) 2
C) 3
D) 4

Solution: B

Scenario 2 and 4 possessed same validation accuracies yet we will select 2 because depth is lower is beter hyper key.

Complete Distribution

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It cans access the scores here. More than 350 people involved in which skill test and the highest grade obtained been 28.

End Notes

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