Isolation Forest The Next CEO of Stack Overflow2019 Community Moderator ElectionIsolation Forest height limit absent in SkLearn implementationIsolation forest results every value -1Multivariate outlier detection with isolation forest..How to detect most effective features?

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Isolation Forest



The Next CEO of Stack Overflow
2019 Community Moderator ElectionIsolation Forest height limit absent in SkLearn implementationIsolation forest results every value -1Multivariate outlier detection with isolation forest..How to detect most effective features?










0












$begingroup$


Can some one please explain Isolation Forests more clearly? Everywhere I search, I find the same explanation:




Isolation Forest ‘isolates’ observations by randomly selecting a
feature and then randomly selecting a split value between the maximum
and minimum values of the selected feature.




Let's take an example to solve this:



x1 = [2, 1, 4, 6, 4, 2, 1, 2, 3, 4, 19]


How would I say that 19 is an outlier?










share|improve this question









New contributor




Shyam Kishor is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.







$endgroup$
















    0












    $begingroup$


    Can some one please explain Isolation Forests more clearly? Everywhere I search, I find the same explanation:




    Isolation Forest ‘isolates’ observations by randomly selecting a
    feature and then randomly selecting a split value between the maximum
    and minimum values of the selected feature.




    Let's take an example to solve this:



    x1 = [2, 1, 4, 6, 4, 2, 1, 2, 3, 4, 19]


    How would I say that 19 is an outlier?










    share|improve this question









    New contributor




    Shyam Kishor is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
    Check out our Code of Conduct.







    $endgroup$














      0












      0








      0





      $begingroup$


      Can some one please explain Isolation Forests more clearly? Everywhere I search, I find the same explanation:




      Isolation Forest ‘isolates’ observations by randomly selecting a
      feature and then randomly selecting a split value between the maximum
      and minimum values of the selected feature.




      Let's take an example to solve this:



      x1 = [2, 1, 4, 6, 4, 2, 1, 2, 3, 4, 19]


      How would I say that 19 is an outlier?










      share|improve this question









      New contributor




      Shyam Kishor is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.







      $endgroup$




      Can some one please explain Isolation Forests more clearly? Everywhere I search, I find the same explanation:




      Isolation Forest ‘isolates’ observations by randomly selecting a
      feature and then randomly selecting a split value between the maximum
      and minimum values of the selected feature.




      Let's take an example to solve this:



      x1 = [2, 1, 4, 6, 4, 2, 1, 2, 3, 4, 19]


      How would I say that 19 is an outlier?







      data-science-model outlier






      share|improve this question









      New contributor




      Shyam Kishor is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.











      share|improve this question









      New contributor




      Shyam Kishor is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.









      share|improve this question




      share|improve this question








      edited 36 mins ago









      Stephen Rauch

      1,52551330




      1,52551330






      New contributor




      Shyam Kishor is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.









      asked 1 hour ago









      Shyam KishorShyam Kishor

      1




      1




      New contributor




      Shyam Kishor is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.





      New contributor





      Shyam Kishor is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.






      Shyam Kishor is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.




















          1 Answer
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          0












          $begingroup$

          Isolation Forrests can be easily thought of as a Tree based method for finding outliers. As you stated, the algorithm works by randomly selecting a feature and then partitions the data like a regular Decision Tree would. The idea is to see how much "depth" is required to get purity. Said another way, many binary decision lines would have to be drawn to isolate observations towards the middle, versus only one line may be necessary for an observation toward the outside.



          You can see this visually from the pictures below:



          enter image description here



          One of the benefits to using this method of outlier detection, relative to others, is that it has the potential to have a relatively quick outlier detection. Only a few binary lines may be necessary to detect an outlier (as shown in the second picture).



          As far as implementation, you can read about this further on the scikit-learn docs here.



          The original paper here may also be helpful.



          Source: Isolation Trees (paper)






          share|improve this answer











          $endgroup$













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            0












            $begingroup$

            Isolation Forrests can be easily thought of as a Tree based method for finding outliers. As you stated, the algorithm works by randomly selecting a feature and then partitions the data like a regular Decision Tree would. The idea is to see how much "depth" is required to get purity. Said another way, many binary decision lines would have to be drawn to isolate observations towards the middle, versus only one line may be necessary for an observation toward the outside.



            You can see this visually from the pictures below:



            enter image description here



            One of the benefits to using this method of outlier detection, relative to others, is that it has the potential to have a relatively quick outlier detection. Only a few binary lines may be necessary to detect an outlier (as shown in the second picture).



            As far as implementation, you can read about this further on the scikit-learn docs here.



            The original paper here may also be helpful.



            Source: Isolation Trees (paper)






            share|improve this answer











            $endgroup$

















              0












              $begingroup$

              Isolation Forrests can be easily thought of as a Tree based method for finding outliers. As you stated, the algorithm works by randomly selecting a feature and then partitions the data like a regular Decision Tree would. The idea is to see how much "depth" is required to get purity. Said another way, many binary decision lines would have to be drawn to isolate observations towards the middle, versus only one line may be necessary for an observation toward the outside.



              You can see this visually from the pictures below:



              enter image description here



              One of the benefits to using this method of outlier detection, relative to others, is that it has the potential to have a relatively quick outlier detection. Only a few binary lines may be necessary to detect an outlier (as shown in the second picture).



              As far as implementation, you can read about this further on the scikit-learn docs here.



              The original paper here may also be helpful.



              Source: Isolation Trees (paper)






              share|improve this answer











              $endgroup$















                0












                0








                0





                $begingroup$

                Isolation Forrests can be easily thought of as a Tree based method for finding outliers. As you stated, the algorithm works by randomly selecting a feature and then partitions the data like a regular Decision Tree would. The idea is to see how much "depth" is required to get purity. Said another way, many binary decision lines would have to be drawn to isolate observations towards the middle, versus only one line may be necessary for an observation toward the outside.



                You can see this visually from the pictures below:



                enter image description here



                One of the benefits to using this method of outlier detection, relative to others, is that it has the potential to have a relatively quick outlier detection. Only a few binary lines may be necessary to detect an outlier (as shown in the second picture).



                As far as implementation, you can read about this further on the scikit-learn docs here.



                The original paper here may also be helpful.



                Source: Isolation Trees (paper)






                share|improve this answer











                $endgroup$



                Isolation Forrests can be easily thought of as a Tree based method for finding outliers. As you stated, the algorithm works by randomly selecting a feature and then partitions the data like a regular Decision Tree would. The idea is to see how much "depth" is required to get purity. Said another way, many binary decision lines would have to be drawn to isolate observations towards the middle, versus only one line may be necessary for an observation toward the outside.



                You can see this visually from the pictures below:



                enter image description here



                One of the benefits to using this method of outlier detection, relative to others, is that it has the potential to have a relatively quick outlier detection. Only a few binary lines may be necessary to detect an outlier (as shown in the second picture).



                As far as implementation, you can read about this further on the scikit-learn docs here.



                The original paper here may also be helpful.



                Source: Isolation Trees (paper)







                share|improve this answer














                share|improve this answer



                share|improve this answer








                edited 35 mins ago

























                answered 41 mins ago









                EthanEthan

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                    ValueError: Expected n_neighbors <= n_samples, but n_samples = 1, n_neighbors = 6 (SMOTE) The 2019 Stack Overflow Developer Survey Results Are InCan SMOTE be applied over sequence of words (sentences)?ValueError when doing validation with random forestsSMOTE and multi class oversamplingLogic behind SMOTE-NC?ValueError: Error when checking target: expected dense_1 to have shape (7,) but got array with shape (1,)SmoteBoost: Should SMOTE be ran individually for each iteration/tree in the boosting?solving multi-class imbalance classification using smote and OSSUsing SMOTE for Synthetic Data generation to improve performance on unbalanced dataproblem of entry format for a simple model in KerasSVM SMOTE fit_resample() function runs forever with no result