Dear Abdul,

I think that what you're looking at is how to set a priori class
probabilities in Random Forest. There is a SetPriors method in the
RandomForestsMachineLearningModel class in OTB which is doing that:

https://www.orfeo-toolbox.org/doxygen/classotb_1_1RandomForestsMachineLearningModel.html#ac4591a82739dd4176abf149b11cd438c

I'll give you here the documentation of the 'priors' attribute which is
available in the source code of the RandomForestsMachineLearningModel class:

/** The array of a priori class probabilities, sorted by the class label
   * value. The parameter can be used to tune the decision tree preferences
toward
   * a certain class. For example, if you want to detect some rare anomaly
   * occurrence, the training base will likely contain much more normal
cases than
   * anomalies, so a very good classification performance will be achieved
just by
   * considering every case as normal. To avoid this, the priors can be
specified,
   * where the anomaly probability is artificially increased (up to 0.5 or
even
   * greater), so the weight of the misclassified anomalies becomes much
bigger,
   * and the tree is adjusted properly. You can also think about this
parameter as
   * weights of prediction categories which determine relative weights that
you
   * give to misclassification. That is, if the weight of the first
category is 1
   * and the weight of the second category is 10, then each mistake in
predicting
   * the second category is equivalent to making 10 mistakes in predicting
the
   * first category. */

Regards,

2016-10-20 15:11 GMT+02:00 Abdul Majeed <[email protected]>:

> I need some help in assigning weight to each variable in RF and its impact
> on RF predictions, the internal mechanism of weight?
>
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-- 
Manuel Grizonnet

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