8 of the 43 questions in this set, with the correct answer marked and explained.
1. Is Random Forest Algorithms same as Bagging Decision Tree ?
✓No
Random Forest Classifier is similar to Bagging of Decision Trees but with a slight difference . RF additionally allows sampling of features and hence each estimator of RF may see a subset of features
✗Yes
2. Neural style transfer is trained as a supervised learning task in which the goal is to input two images (x), and train a network to output a new, synthesized image (y).
✓False
Neural style transfer is about training on the pixels of an image to make it look artistic, it is not learning any parameters.
✗True
3. Which of these is a more accurate description of a data-centric approach to ML development?
✓Holding the neural network architecture fixed, work to improve the data to do well on the problem.
That's right! Data-centric means you focus your efforts on improving the data to raise the system's performance, while keeping the code fixed.
✗Holding the training data fixed, work to improve your neural network’s architecture to do well on the problem.
4. True or False? During error analysis, each example should only be assigned one tag. For example, in a speech recognition application you may have the tags: "car noise", "people noise" and "low bandwidth". If you encounter an example with both car noise and low bandwidth audio, you should use your judgement to assign just one of these two tags rather than apply both tags.
✗True
✓False
Each example should have as many tags as is necessary to accurately classify it. This will help you develop an accurate understanding of where your errors are coming from, which will in turn help you focus your efforts in reducing the errors.
5. If searching among a large number of hyperparameters, you should do a systematic grid search rather than start from random values, so that you are not relying on chance. True or False?
✗True
✓False
Because you don't know which hyper-parameter's are more important than others, use a smart algorithm that can choose the direction and granularity of your search. In such a smart algorithm, it is a good idea to start with well-distributed, random initial starting points.
6. Select all the true statements on Network Graph from below.
✗Connections between a set of items in the network are called vertices
✓Weighted networks are used to describe networks with unequal relationships between nodes.
All the Options are correct except "Connections between a set of items in the network are called vertices" - they are called edges.
✗When there are only two opposite relationships between nodes, a signed network is a good representation.
✓An undirected graph is a good choice to present a network with asymmetric relationships between nodes
7. Select all true statements on Network Graph from below:
✗Suppose G is a graph and node A, B are two of G’s nodes. G.edge[‘A’][‘B’] and G.edge[‘B’][‘A’] will return the same value for all types of networks.
✓Edges can carry many labels or attributes.
All the options are correct except "Suppose G is a graph and node A, B are two of G’s nodes. G.edge[‘A’][‘B’] and G.edge[‘B’][‘A’] will return the same value for all types of networks." - this is false for directed graph where edge weight will depend on the direction of traversal.
✓Suppose we have created a nx.Graph() object G with some nodes and edges. The statement G.nodes(data=True) will return a list of tuples.
✓Accessing node or edge attributes in NetworkX is the same as accessing values in a Python dictionary
8. Which of the following is true about network robustness and connectivity? Select all that apply
✓The closure of an airport and the cancellation of a ight route are examples of two different kinds of network attacks in the real world
network robustness and connectivity
✗Adding more edges to a network always makes it less robust
✓Adding edges to a network can never make the network less robust.
✓Network robustness measures a network’s ability to maintain its connectivity.
35 more questions in the app
Practise the full 43-question set with a timer, scoring and progress tracking.