gs gs126 Decision Trees - Random Forest Algorithm - Quiz No.1
gs gs126 Neural Networks Quiz
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Question 2: Which of the following statements is not true about the Random forests?
It is a classifier consisting of a collection of decision trees
Each tree is constructed by applying an algorithm on the training set and an additional random vector
The prediction of the random forest is obtained by a majority vote over the predictions of the individual trees
Each individual tree in the random forest will not spits out a class prediction
Question 3: Which of the following statements is not true about the Random forests?
It is an ensemble learning method for classification only
It operates by constructing a multitude of decision trees at training time
It outputs the class that is the mode of the classes
It outputs the mean prediction of the individual trees
Question 4: Which of the following statements is not true about Random forests?
Scaling of data required in random forest algorithm
It works well for a large range of data items than a single decision tree
It has less variance than single decision tree
Random forests are very flexible and possess very high accuracy
Question 5: Which of the following statements is not true about Random forests?
It has high complexity
Construction of Random forests are much easier than decision trees
Construction of Random forests are time-consuming than decision trees
More computational resources are required to implement Random Forest algorithm
Question 7: A data set T is split into two subsets T1 and T2 with sizes N1 and N2. And Gini index of the split data contains examples from N classes. Then the Gini index of T is defined by which of the following options?
Ginisplit (T) = \(\frac {N_2}{N}\) gini (T1) + \(\frac {N_1}{N}\) gini (T2)
Ginisplit (T) = \(\frac {N}{N_1}\) gini (T1) + \(\frac {N}{N_2}\) gini (T2)
Ginisplit (T) = \(\frac {N}{N_2}\) gini (T1) + \(\frac {N}{N_1}\) gini (T2)
Ginisplit (T) = \(\frac {N_1}{N}\) gini (T1) + \(\frac {N_2}{N}\) gini (T2)
Question 10: Which of the following is represented by the below figure?
Support vector machine
Random forest
Regression tree
Classification tree
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