Saturday, November 19, 2022

Car Evaluation Dataset - Random Forest Classification

Car Evaluation Database :

The car evaluation database is derived from a simple hierarchical decision-making model initially developed to represent expert decision-making systems. Characteristics include purchase price, maintenance costs, number of doors, number of passengers it can carry, size of the luggage compartment, and estimated vehicle safety. The target class is unacceptable, acceptable, good, and very good.

Random Forest Classifier
A random forest is a meta-estimator that fits a set of decision tree classifiers to different subsamples of a dataset, using averaging to improve prediction accuracy and control overfitting. The subsample size is controlled by the max_samples parameter. Otherwise, use the entire dataset to build each tree. Basically, we take a set of decision trees from a randomly selected subset of the training set and collect votes from different decision trees to make a final prediction.


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