Machine Learning

 Machine Learning MCQs - 5  (Ensemble Models) --------------------------------------------------------------------- 1.  The model which consists of a group of predictors is called a Group Entity Ensemble Set Ans: 3 2.  A Random forest is an ensemble of Decision Trees True False Ans: 1 3.  The steps involved in deciding the output of a Random Forest are Obtain the predictions of all individual trees Predict the class that gets the most votes Both of the above Ans: 3 4.  A hard voting classifier takes into consideration The probabilities of output from each classifier The majority votes from the classifiers The mean of the output from each classifier The sum of the output from each classifier Ans: 2 5.  If each classifier is a weak learner, the ensemble can still be a strong learner? True False Ans: 1 6.  Ensemble methods work best when the predictors are Sufficiently diverse As independent from one another as possible Making very different types of errors All of the above Ans: 4 7.  To

 Machine Learning MCQs - 4  (Clustering, Dimensionality Reduction) --------------------------------------------------------------------- 1.  Which of the following is finally produced by Hierarchical Clustering? final estimate of cluster centroids tree showing how close things are to each other assignment of each point to clusters all of the mentioned Ans: 2 2.  Which of the following is required by K-means clustering? defined distance metric number of clusters initial guess as to cluster centroids all of the mentioned Ans: 4 3.  Point out the wrong statement. k-means clustering is a method of vector quantization k-means clustering aims to partition n observations into k clusters k-nearest neighbor is same as k-means none of the mentioned Ans: 3 4.  Which of the following combination is incorrect? Continuous – euclidean distance Continuous – correlation similarity Binary – manhattan distance None of the mentioned Ans: 4 5.  Hierarchical clustering should be primarily used for explorati