Question 29 4 pts Assume the image below is a representation of one leaf node (among many leaf nodes) in a classification decision tree (having only two classes: + and -). Which of the following information can we get from this image? Select ALL that apply. Information gain Entropy The probability of being classified as positive when a data point is assigned to this specific leaf node Model accuracy given the impurity of this leaf node
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Consider the training data shown in the following for a binary classification problem. Type of Iris is the response variable, Sepal Length is the splitting variable. Instance Type of Iris Sepal Length (in cm) 1 Setosa 4.5 2 Setosa 4.7 3 Setosa 4.8 4 Setosa 5.3 5 Setosa 5.5 6 Virginica 5.1 7 Virginica 5.7 8 Virginica 6.9 9 Virginica 7.6 10 Virginica 7.7 Given the following two possible splits, Split one: Child node 1: Sepal Length ≤ 4.8 Child node 2: Sepal Length > 4.8 Split Two: Child node 1: Sepal Length ≤ 5.5 Child node 2: Sepal Length > 5.5 Please do the following: (a) (10 points) Calculate the entropy of the two splits. Which split is better according to entropy? (b) (10 points) Calculate the Gini index of the two splits. Which split is better according to Gini?
Madhur L.
Shu N.
Which of the following statements ARE CORRECT? a. Node impurity measures (such as Gini or Entropy) tend to prefer splits that result in large number of partitions. b. Information Gain is the expected reduction in entropy caused by partitioning the examples according to a given attribute. c. Entropy at a given note t, can have a minimum value of 0.5 - when all records belong to one class - implying most beneficial situation for classification. d. Classification error at a node t has a minimum value of 1-1/c when records are equally distributed among all classes.
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