In this section, we will run a random forest regression for the Boston dataset; the median values of owner-occupied homes are predicted for the test data. The dataset describes 13 numerical properties of houses in Boston suburbs, and is concerned with modeling the price of houses in those suburbs in thousands of dollars. As such, this is a regression predictive modeling problem. Input attributes include features like crime rate, proportion of non-retail business acres, chemical concentrations, and more.
To get the data, we draw on the large collection of data available in the UCI Machine Learning Repository at the following link: http://archive.ics.uci.edu/ml
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