How is the best split determined at each node while building a Decision Tree?Answer choicesSelect only one optionREVISITWe split the data using the first independent variable and so on. based on the alphabetical orderThe first split is determined randomly and from then on we start choosing the best split.We make at most 5 splits on the data using only one independent variable and choose the split that gives the highest Information Gain.

How can you determine the split for each node of a decision tree? 1 pointFind the split that induces the largest entropy.Randomly select the split.Find the split that minimizes the gini impurity. Use a nonlinear decision boundary to find the best split.

When evaluating all possible splits of a decision tree what can be used to find the best split regardless of what happened in prior or future steps?1 pointGreedy SearchRegularizationClassificationLogistic regression

Which of the following statements is not true about the Decision tree?1 pointa) It starts with a tree with a single leaf and assign this leaf a label according to a majority vote among all labels over the training setb) It performs a series of iterations and on each iteration, it examine the effect of splitting a single leafc) It defines some gain measure that quantifies the improvement due to the splitd) Among all possible splits, it either choose the one that minimizes the gain and perform it, or choose not to split the leaf at all

How does the use of a "splitting criterion" in decision trees resemble the partitioning step in comparison-based sorting algorithms?*1 pointBoth involve breaking down a dataset into smaller subsets based on certain conditions.Both ensure that the depth of the structure remains minimal.Both focus on identifying outliers within the data.Both aim to increase the number of comparisons made during processing.

Question 2When splitting data into branches for a decision tree, what kind of feature is favored and chosen first?1 pointThe feature that increases purity in the tree nodes.The feature with the greatest number of categories.The feature that splits the data equally into groups.The feature that increases entropy in the tree nodes.