If the training set is such that every combination of attribute valuesis present in the training data and each combination is either labeled positive or negative, which of the following classification techniques can be used to learn a model with perfect classification on the training set? Briefly explain your answer. a. Decision Treesb. Logistic Regressionc. NaiveBayesd. multi-layer ANNe. perceptron

.a. Decision Trees

Explanation:

Decision Trees (DTs) are predictive modeling approaches used in machine learning and artificial intelligence to make decisions(if else) in a tree like model. Decision trees observes and learns from data to conclude on item values. It learns decision rules from data to predict values for items based on prior data. The deeper the tree, the more complex the decision rules.

the answer is gallery.

which functions return a valid value without any arguments?

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