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Image Classification MCQ's

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

   View Answer   

   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 __________.

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   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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