When performing regression or classification, which of the following is the correct way to preprocess the data?

Normalize the data -> PCA -> training

Which of the following is a disadvantage of decision trees?

Decision trees are prone to be overfit

How can you prevent a clustering algorithm from getting stuck in bad local optima?

Use multiple radom initializations

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Prelim Exam of ML on 28/04/2020 Time 10.00 AM

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Questions 20

Answers

1
Why ensemble learning is used
All of the above
function approximation
improve the classification
prediction
2
What is not Machine Learning?
Both A and B
Rule based inference
Artificial Intelligence
None of the above
3
When performing regression or classification, which of the following is the correct way to preprocess the data?
Normalize the data -> PCA -> normalize PCA output -> training
Normalize the data -> PCA -> training
None of the above
PCA -> normalize PCA output -> training
4
what are the various approaches for machine learning?
Concept Vs Classification Learning
All of the above
Inductive Vs Analytical Learning
Symbolic Vs Statistical Learning
5
What are the popular algorithms of Machine Learning
None of the above
Nearest Neighbor ,Support vector machines
Both A and B
• Decision Trees • Neural Networks (back propagation)
6
How do you handle missing or corrupted data in a dataset?
Assign a unique category to missing values
Replace missing values with mean/median/mode
Drop missing rows or columns
All of the above
7
Which of the following is a widely used and effective machine learning algorithm based on the idea of bagging?
Random Forest
Classification
Decision Tree
Regression
8
Which of the following is true about Naive Bayes ?
Both A and B
none of the mentioned
Assumes that all the features in a dataset are independent
Assumes that all the features in a dataset are equally important
9
Which of the following is a disadvantage of decision trees?
Decision trees are prone to be overfit
Factor analysis
Decision trees are robust to outliers
None of the above
10
which alg is used in K-means clustering
shortest path alg
None of the above
euclidean distance
dijkstra's alg
11
Which of the following is an example of feature extraction?
Removing stopwords in a sentence
function approximation
Applying PCA projects to a large high-dimensional data
Constructing bag of words vector from an email
12
Why is second order differencing in time series needed?
Both A and c
To find the maxima or minima at the local point
None of these
To remove stationarity
13
In what areas Pattern Recognition is used?
Statistics,Informal Retrieval,Bio-Informatics
None of the above
Both A and B
Computer Vision,Speech Recognition,Data Mining
14
What are the three stages to build the hypotheses or model in machine learning
Model testing
Applying the model
All of the above
Model building
15
Which of the following techniques can be used for normalization in text mining?
Stop Word Removal
Both A and B
Lemmatization
Stemming
16
what is the function of ‘Unsupervised Learning’?
All of the above
Find clusters of the data
Find novel observations/ database cleaning
Find low-dimensional representations of the data
17
Which of the following is a good test dataset characterstic?
Is representative of the dataset as a whole
None of the above
Large enough to yield meaningful results
Both A and B
18
The most widely used metrics and tools to assess a classification model are:
Area under the ROC curve
at fixed time intervals
Confusion matrix
All of the above
19
What are the different Algorithm techniques in Machine Learning
All of the above
Supervised Learning ,Unsupervised Learning,
Semi-supervised Learning,Learning to Learn
Reinforcement Learning,,Transduction
20
How can you prevent a clustering algorithm from getting stuck in bad local optima?
Set the same seed value for each run
None of the above
Use multiple radom initializations
Both A and B

Prelim Exam of ML on 28/04/2020 Time 10.00 AM Flashcards | StudBud