ML Course Activity-II

_______ is basically a type of unsupervised learning method.

• Un-supervised Learning

• Clustering

• Supervised Learning

• All

_____ in which we will fit the data on the probability that how it may belong to the same distribution

• Centroid-based methods                              

• Connectivity-based methods

• Distribution-based model

• All

__________clusters formed in this method form a tree-type structure based on the hierarchy.       

• Density-Based                                            

• Image-Based

• Grid-based                                                  

• Hierarchical Based

__________consider the clusters as the dense region having some similarities and differences from the lower dense region of the space.  

• Density-Based 

• Hierarchical Based

• Grid-based                   

• None of these

Choose from the following that is Decision Tree nodes.                                                                   

• Decision Nodes                                           

• End Nodes

• Chance Nodes                                            

• All of the mentioned

What is Decision Tree?

• Flow-Chart & Structure in which the internal node represents a test on an attribute, each branch represents the outcome of the test and each leaf node represents the class label

• Structure in which the internal node represents a test on an attribute, each branch represents the outcome of the test and each leaf node represents a class label

• Flow-Chart

• All the above

Decision makers who are concerned with tactical (short-term) operational problems and decision making is  

• Executive managers

• Mobile managers 

• Middle managers

• Supervisors    

If you use an ensemble of different base models, is it necessary to tune the hyperparameters of all base models to improve the ensemble performance?   

• Yes

• No

• Can't say

• None

Generally, an ensemble method works better, if the individual base models have ____________

• Correlation does not have any impact on ensemble output

• Less correlation among predictions

• High correlation among predictions 

• All the above

Which of the following algorithm is not an example of an ensemble method? 

• Gradient Boosting

• Extra Tree Regressor    

• Random Forest

• Decision Tree