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. 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. computer science 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. 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.
computer science
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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