This is not supported in MLlib. Hopefully, we will add support for
weighted examples in v1.2. If you want to train weighted instances
with the current tree implementation, please try importance sampling
first to adjust the weights. For instance, an example with weight 0.3
is sampled with
will be released? We can plan things
accordingly.
Date: Tue, 2 Sep 2014 23:15:09 -0700
Subject: Re: MLLib decision tree: Weights
From: men...@gmail.com
To: ssti...@live.com
CC: user@spark.apache.org
This is not supported in MLlib. Hopefully, we will add support for
weighted examples
Hi everyone,
We are looking to apply a weight to each training example; this weight should
be used when computing the penalty of a misclassified example. For instance,
without weighting, each example is penalized 1 point when evaluating the model
of a classifier, such as a decision
