1. Identify the unstructured data from the following
View Answer
both image and Video clip
2. Which preprocessing technique is used for dimensinality reduction?
View Answer
SVD
3. Technique used to evaluate a classifier by dividing the data set into train set to train the classifier and test set to test the same.
View Answer
cross validation
4. True Negative is when
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predic are and actual negative
5. True Positive is when
View Answer
predic are and actual not negative
6. The scale-invariant feature transform can be used to detect and describe local features in images
View Answer
True
7. Images, documents are examples of ___________.
View Answer
Unstrucruted data
8. The improvement of the image data that suppresses distortions or enhances image features is called ____________.
View Answer
All of the above
9. The variation present in the PCs decrease as we move from the 1st PC to the last one.
View Answer
True
10. Which classifier involves finding Optimal hyperplane for linearly separable Patterns?-
View Answer
SVM
11. TF-IDF is a common methodology used in pre-processing of images.
View Answer
True
12. Scale-
View Answer
Invariant Feature Transform
13. PCA stands for _________.
View Answer
Principle Component Analysis
14. SVM is a __________.
View Answer
Support Vector Machine- unsupervised learning
15. Choose the correct sequence for classifier building from the following:
View Answer
Initialize-train -predict -examples
16. Clustering is a supervised classification.
View Answer
False
17. The normalized linear combination of the original predictors in a data set is called ___________
View Answer
Principle components
18. The most widely used package for machine learning in python is ____________.
View Answer
pillow
19. Select the correct option that directly achieves multi-class classification (without support of binary classifiers).
View Answer
K nearest neighbour
20. Classification where each data is mapped to more than one class is called ____________.
View Answer
Binary classification
21. Pruning is a technique associated with ______________.
View Answer
Decision trees
22. GradientDescent is one of Backward propagation techniques to find the best set of parameters of the network.
View Answer
True
23. In Supervised learning, class labels of the training samples are ___________.
View Answer
known
24. A classifer that can compute using numeric as well as categorical values is _________.
View Answer
random forest-- decison tree
25. Which of the given hyper parameter(s), when increased may cause random forest to over fit the data?
View Answer
Depth of tree
26. Which of the following is not a preprocessing technique used for image processing?
View Answer
None
27. Higher value of which of the following hyperparameters is better for decision tree algorithm
View Answer
cannot say
28. HOG is simplified version of SIFT.
View Answer
False
29. Which one of the following is not a classification technique?
View Answer
StratifiedShuffleSplit
30. The process of changing the pixel intensity values to achieve consistency in dynamic range for images is ___________.
View Answer
Image normalisation
31. Which of the following is not a characteristics of HOG?
View Answer
Used in sliding window fashion
32. Supervised learning differs from unsupervised learning. Supervised learning requires ____________
View Answer
labeled data
33. Which of the following is a feature extraction technique?
View Answer
all
34. Which of the following is not a performance evaluation measure?
View Answer
decision tree
35. What is the function that converts K-dimensional vector containing real values to the same shaped vector of real values in the range of (0,1), whose sum is 1?
View Answer
Softmax
36. HOG refers to _________.
View Answer
Histogram of oriented gradients
37. Which among the following is True?
View Answer
A. SIFT is used for identification of specific objects B. HOG is used for classification-- both
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