[scikit-learn] Interpreting results of random forest classifier
niourf at gmail.com
Mon Dec 28 07:41:46 EST 2020
> As I understand it now the 0 probability is probability that the
prediction is wrong, and the 1 probability is the probability that the
prediction is correct
No: in binary classification, the `predict_proba` method returns a
single number in [0, 1] indicating the probability that the sample
belongs to the positive class (1). In other words, (proba <= 0.5 iff
prediction == 0). The threshold is 0.5 since there are only 2 classes.
> One thing I do not understand is why the probability ranges do not go
from 0 to 100; they go from 0 to 49 for 0 probability and 49-100 for 1
This is a correct observation and it's a direct consequence of the above
The way probabilities are computed is briefly described here:
On 12/28/20 2:36 AM, DAVID cofield wrote:
> Could someone explain what the probability values provided by the
> random forest classifier represents?
> When I run the classifier with two classes, I get prediction values
> and associated to these prediction values are probabilities. As I
> understand it now the 0 probability is probability that the prediction
> is wrong, and the 1 probability is the probability that the prediction
> is correct. One thing I do not understand is why the probability
> ranges do not go from 0 to 100; they go from 0 to 49 for 0 probability
> and 49-100 for 1 probability. How do I interpret the probabilities?
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> Windows 10
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> scikit-learn at python.org
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