Identify the unstructured data from the following Both image and video clip
Which preprocessing technique is used for dimensinality reduction? SVD
True Negative is when The predicted instance and the actual instance are negative
True Positive is when The predicted instance and the actual instance are not negative
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. Cross validation
Which of the following is not a preprocessing technique used for image processing?
None
HOG is simplified version of SIFT False
HOG refers to _________. Histogram of oriented gradients
Which of the following is a feature extraction technique? All
Which of the following is not a characteristics of HOG? Invariant to common...
PCA stands for _________. Principal component analysis
Which classification techniques involves finding the eigenvalues and eigenvectors?
PCA
In SVD, the matrix A of dimension m x n can be decomposed in to A=USVT, where
V is a ___________. n x n orthogonal matrix
Which one of the following is not a classification technique? StratifedShuffleSplit
Classification where each data is mapped to more than one class is called
____________. Binary Classification
In Supervised learning, class labels of the training samples are ___________ known
The process of changing the pixel intensity values to achieve consistency in
dynamic range for images is ___________. image normalization
Choose the correct sequence for classifier building from the following Initialize →
train → predict → evaluate
Which of the following is not a performance evaluation measure? Decision tree
GradientDescent is one of Backward propagation techniques to find the best set of
parameters of the network. True
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? Softmax
Clustering is a supervised classification. False
Choose the right options based on Pooling. All
A technique used to depict the performance in a tabular form that has 2
dimensions namely “actual” and “predicted” sets of data is called ___________.
confusion matrix
Which algorithm can be used for matching local regions in two images? Surf
Which of the given hyper parameter(s), when increased may cause random forest to
over fit the data? Depth of tree
Select the correct option that directly achieves multi-class classification (without
support of binary classifiers). K nearest neighbour
Model Tuning helps to increase the accuracy True
The normalized linear combination of the original predictors in a data set is called
____________ Principal components
SIFT computes the gradient histogram only for patches where as HOG is computed
for an entire image. False
High classification accuracy always indicates a good classifier. False
Higher value of which of the following hyperparameters is better for decision tree
algorithm? Can't say
SVM is a __________ Supervised learning algorithm
The scale-invariant feature transform can be used to detect and describe local
features in images. True
Unsupervised classification identifies larger number of spectrally-distinct classes
than supervised classification. True
SIFT stands for ________________ Scale Invariant Feature Transform
Which of the following is not an example of CNN architectures None
The fit(X, y) is used to ___________ Train the Classifier
Which classifier involves finding Optimal hyperplane for linearly separable
Patterns?
SVM
The first layer in a CNN is never a Convolutional Layer. False.
TF-IDF is a common methodology used in pre-processing of images False
Images, documents are examples of ___________. Un-structured data
The most widely used package for machine learning in python is ____________
sklearn
