LocalOutlierFactor scikit-learn
My goal is to use the LocalOutlierFactor class from scikit-learn to do real-time Novelty Detection. This can be achieved by setting novelty=True
in the constructor, though I'm a little bit confused by the parameter contamination
.
Such parameter, which must be in (0, 0.5], is used to specify the amount of outliers in the training set. This makes perfect sense when in an Outlier Detection setting (i.e. when novelty=False
is passed to the constructor of LocalOutlierFactor), but I can't understand its purpose in a Novelty Detection setting.
The point is that I'm assuming there are no outliers in the training set, but at the same time I cannot just set contamination=0
because it must be in (0, 0.5]. I know that I could set it to a very small value like 1e-16
or something, but it feels like a hack to me. So what it is used for in a Novelty Detection setting?
python scikit-learn outliers novelty-detection
add a comment |
My goal is to use the LocalOutlierFactor class from scikit-learn to do real-time Novelty Detection. This can be achieved by setting novelty=True
in the constructor, though I'm a little bit confused by the parameter contamination
.
Such parameter, which must be in (0, 0.5], is used to specify the amount of outliers in the training set. This makes perfect sense when in an Outlier Detection setting (i.e. when novelty=False
is passed to the constructor of LocalOutlierFactor), but I can't understand its purpose in a Novelty Detection setting.
The point is that I'm assuming there are no outliers in the training set, but at the same time I cannot just set contamination=0
because it must be in (0, 0.5]. I know that I could set it to a very small value like 1e-16
or something, but it feels like a hack to me. So what it is used for in a Novelty Detection setting?
python scikit-learn outliers novelty-detection
add a comment |
My goal is to use the LocalOutlierFactor class from scikit-learn to do real-time Novelty Detection. This can be achieved by setting novelty=True
in the constructor, though I'm a little bit confused by the parameter contamination
.
Such parameter, which must be in (0, 0.5], is used to specify the amount of outliers in the training set. This makes perfect sense when in an Outlier Detection setting (i.e. when novelty=False
is passed to the constructor of LocalOutlierFactor), but I can't understand its purpose in a Novelty Detection setting.
The point is that I'm assuming there are no outliers in the training set, but at the same time I cannot just set contamination=0
because it must be in (0, 0.5]. I know that I could set it to a very small value like 1e-16
or something, but it feels like a hack to me. So what it is used for in a Novelty Detection setting?
python scikit-learn outliers novelty-detection
My goal is to use the LocalOutlierFactor class from scikit-learn to do real-time Novelty Detection. This can be achieved by setting novelty=True
in the constructor, though I'm a little bit confused by the parameter contamination
.
Such parameter, which must be in (0, 0.5], is used to specify the amount of outliers in the training set. This makes perfect sense when in an Outlier Detection setting (i.e. when novelty=False
is passed to the constructor of LocalOutlierFactor), but I can't understand its purpose in a Novelty Detection setting.
The point is that I'm assuming there are no outliers in the training set, but at the same time I cannot just set contamination=0
because it must be in (0, 0.5]. I know that I could set it to a very small value like 1e-16
or something, but it feels like a hack to me. So what it is used for in a Novelty Detection setting?
python scikit-learn outliers novelty-detection
python scikit-learn outliers novelty-detection
asked Jan 2 at 20:14
Francesco CariaggiFrancesco Cariaggi
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