Abstract
This chapter discusses the use of decision forests for the probabilistic estimation of continuous variables. Regression forests are used for the non-linear regression of dependent variables given independent input, where both input and output may be multi-dimensional. As with the other chapters we start with a brief literature survey of linear and non-linear regression techniques. We then describe the regression forest model, and finally we demonstrate its properties with a number of illustrative examples. Exercises are presented in the final section.
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Notes
- 1.
The smoothness of the mean curve is a function of T. In general, the larger the forest size the smoother the mean prediction curve.
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Criminisi, A., Shotton, J. (2013). Regression Forests. In: Criminisi, A., Shotton, J. (eds) Decision Forests for Computer Vision and Medical Image Analysis. Advances in Computer Vision and Pattern Recognition. Springer, London. https://doi.org/10.1007/978-1-4471-4929-3_5
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DOI: https://doi.org/10.1007/978-1-4471-4929-3_5
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