Gradient Boosted Trees for Regression in Python

Step-by-step follow-along | Data Series | Episode 11.6

An explanation of Gradient Boosted Trees for Regression: Episode 11.5

You can view and use the code and data in this episode here: Link

Objective

Produce gradient boosted trees to predict diamond prices in dollars given their:

• carat: weight of the diamond (0.2–5.01)
• cut: quality of the cut (Fair, Good, Very Good, Premium, Ideal)
• color: diamond colour, from D (best to ) to J (worst)
• clarity: a measurement of how clear the diamond is (I1 (worst), SI2, SI1, VS2, VS1, VVS2, VVS1, IF (best))
• x: length in mm (0–10.74)
• y: width in mm (0–58.9)
• z: depth in mm (0–31.8)
• depth: % The height of a diamond, measured from the culet to the table, divided by its average girdle diameter
• table: % The width of the diamond’s table expressed as a percentage of its average diameter