Given the dataset of cars with their mileage and cost for cost prediction: Build a decision tree and attach the picture of you decision tree formed as the solution to the question using hints from tutorial_6. What do you think is special about the data type of the values being predicted in this problem? Do a search and find out why a DecisionTreeRegressor is used in this case (instead of the general purpose DecisionTreeClassifier)? Both examples are in the tutorial code. array([['Toyota Corolla', '40', '20175'], ['Ford', '45', '25000'], ['Dodge', '62', '35782'], ['Chevrolet', '50', '30000'], ['Canoo', '57', '34750'], ['Tesla', '113', '54000'], ['BMW', '70', '36400']], dtype='
Given the dataset of cars with their mileage and cost for cost prediction:
Build a decision tree and attach the picture of you decision tree formed as the solution to the question using hints from tutorial_6. What do you think is special about the data type of the values being predicted in this problem? Do a search and find out why a DecisionTreeRegressor is used in this case (instead of the general purpose DecisionTreeClassifier)? Both examples are in the tutorial code.
array([['Toyota Corolla', '40', '20175'],
['Ford', '45', '25000'],
['Dodge', '62', '35782'],
['Chevrolet', '50', '30000'],
['Canoo', '57', '34750'],
['Tesla', '113', '54000'],
['BMW', '70', '36400']], dtype='<U21')
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