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The CART algorithm splits the training set in two subsets
Using all the features and a threshold tk
Using a single feature k and a threshold tk
Using half of the features and a threshold k
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1 Decision Trees
2 Decision Trees can be used for...
3 The iris dataset has...
4 A node’s value attribute tells you how many training instances...
5 A node’s gini attribute measures...
6 If all the training instances of a node belong to...
7 A Gini coefficient of 1 expresses maximal inequality among the...
8 Gini index for a node is found by subtracting the...
9 A white box model's decisions are...
10 A black box model's decisions are...
11 Random Forests and Neural networks are examples of...
12 Decision Trees are examples of...
13 A decision tree estimates the probability that an instance belongs...
14 The Decision Tree classifier predicts the class which has the...
15 If the output of `predict_proba` is `array([[ 0. , 0.90740741,...
16 The CART algorithm splits the training set in two subsets...
17 How does the CART algorithm chooses the feature k and...
18 The cost function for finding the value of feature k...
19 Once the CART algorithm has successfully split the training set...
20 The CART algorithm stops recursion once it reaches the maximum...
21 Which of the following are correct for the CART algorithm...
22 While making a prediction in Decision Tree, each node only...
23 If the total number of training instances is m then...
24 The training algorithm of decision tree compares all features (or...
25 If the number of features in n and number of...
26 Gini impurity is slightly faster to compute in comparision to...
27 Models like Decision Tree models are often called nonparametric model...
28 Which of the following is not a regularization parameter for...
29 Increasing min_* hyperparameters or reducing max_* hyperparameters will regularize the...
30 For regression tasks the CART algorithm tries to split the...
31 All the splits made by a Decision Tree are...
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