Understanding Decision Trees In Machine Learning and How To Implement It In Python Using sklearn

Decision Trees are a type of supervised learning used for classification (yes/no) and regression (continuous data) where the data is continuously split according to a certain parameter. The predicted class is derived from features of the data. The following article creates a Decision Tree from the 311 on 3.11 Project. In this project, the resolution outcome being positive or negative is what is being predicted. Agency: NYPD, Dept of Transportation, Dept of Health & Mental Hygiene, Dept of Sanitation, Dept of Housing Preservation and Development, Dept of Parks and Recreation, etc Borough: Brooklyn, Queens, Manhattan, Bronx, Staten Island Location: Longitude/Latitude, Cross Streets, Intersections Created/Closed Date Complaint Type: Heat/Hot Water, Rodent, Noise, Street Condition, Illegal Parking, Unsanitary Condition, Blocked Driveway are just a few examples.