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Why is my LOF algorithm producing the opposite result it should?


Outlier detection by unsupervised algorithm: Fraud DetectionOutlier detection: Should the metric used in kNN take into account variance explained by each coordinate?Classification based on a Clustering ResultMultivariate outlier detection with isolation forest..How to detect most effective features?What do you call an anomaly/signature detection algorithm in data scienceDo anomalous input features to autoencoder result in high errors on the corresponding output features?Spectral clustering result interpretationWhy is Local Outlier Factor classified as Unsupervised if it requires training data with no outliers?DONUT- Anomaly detection Algorithm ignores the relationship between sliding windows?Algorithm suggestion for anomaly detection in multivariate time series data













0












$begingroup$


What could cause the local outlier factor (LOF) to output below 1.0 for outliers and above 1.0 for inliers? I have my code sort of working just by inverting the output, but I can't figure out what's wrong with my algorithm. I'm following the one Wikipedia lists to the letter, but for some reason, it's producing the opposite result.



On a high, non-code-specific level, does anyone know what could cause the algorithm to behave like this? I've looked everywhere in the code, and to the best of my knowledge, it's doing exactly what this describes.










share|improve this question









$endgroup$







  • 2




    $begingroup$
    Maybe you overlooked the 1/ ? Also, don't rely on Wikipedia, in particular when something is not working, then consult the original works instead...
    $endgroup$
    – Anony-Mousse
    Apr 8 at 5:36















0












$begingroup$


What could cause the local outlier factor (LOF) to output below 1.0 for outliers and above 1.0 for inliers? I have my code sort of working just by inverting the output, but I can't figure out what's wrong with my algorithm. I'm following the one Wikipedia lists to the letter, but for some reason, it's producing the opposite result.



On a high, non-code-specific level, does anyone know what could cause the algorithm to behave like this? I've looked everywhere in the code, and to the best of my knowledge, it's doing exactly what this describes.










share|improve this question









$endgroup$







  • 2




    $begingroup$
    Maybe you overlooked the 1/ ? Also, don't rely on Wikipedia, in particular when something is not working, then consult the original works instead...
    $endgroup$
    – Anony-Mousse
    Apr 8 at 5:36













0












0








0





$begingroup$


What could cause the local outlier factor (LOF) to output below 1.0 for outliers and above 1.0 for inliers? I have my code sort of working just by inverting the output, but I can't figure out what's wrong with my algorithm. I'm following the one Wikipedia lists to the letter, but for some reason, it's producing the opposite result.



On a high, non-code-specific level, does anyone know what could cause the algorithm to behave like this? I've looked everywhere in the code, and to the best of my knowledge, it's doing exactly what this describes.










share|improve this question









$endgroup$




What could cause the local outlier factor (LOF) to output below 1.0 for outliers and above 1.0 for inliers? I have my code sort of working just by inverting the output, but I can't figure out what's wrong with my algorithm. I'm following the one Wikipedia lists to the letter, but for some reason, it's producing the opposite result.



On a high, non-code-specific level, does anyone know what could cause the algorithm to behave like this? I've looked everywhere in the code, and to the best of my knowledge, it's doing exactly what this describes.







clustering anomaly-detection outlier






share|improve this question













share|improve this question











share|improve this question




share|improve this question










asked Apr 8 at 2:16









ZayaZaya

101




101







  • 2




    $begingroup$
    Maybe you overlooked the 1/ ? Also, don't rely on Wikipedia, in particular when something is not working, then consult the original works instead...
    $endgroup$
    – Anony-Mousse
    Apr 8 at 5:36












  • 2




    $begingroup$
    Maybe you overlooked the 1/ ? Also, don't rely on Wikipedia, in particular when something is not working, then consult the original works instead...
    $endgroup$
    – Anony-Mousse
    Apr 8 at 5:36







2




2




$begingroup$
Maybe you overlooked the 1/ ? Also, don't rely on Wikipedia, in particular when something is not working, then consult the original works instead...
$endgroup$
– Anony-Mousse
Apr 8 at 5:36




$begingroup$
Maybe you overlooked the 1/ ? Also, don't rely on Wikipedia, in particular when something is not working, then consult the original works instead...
$endgroup$
– Anony-Mousse
Apr 8 at 5:36










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