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Isolation Forest Prediction Mechanics: Does it compare value with every tree (and the original training subset)?

0

$begingroup$

So I understand the general idea of how isolation forests works, but I'm having trouble understanding how the model makes predictions on new data.

Does it pass every new point (separately) through every tree in the trained model, and then runs the exact same splits with the exact same original subset data (plus this single new point), to determine the number of steps (anomaly score) until this new point gets isolated?
Then this score gets compared back to the anomaly score threshold that was set when the model was trained?

random-forest decision-trees anomaly-detection

share|improve this question

asked Mar 21 '18 at 22:41

Michael DuMichael Du

11

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bumped to the homepage by Community♦ 43 mins ago

This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.

• 
  
  
  

  
  

  
  
  
  
  
  

  
  $begingroup$
  What do you mean by original subset data? Only the trees are remembered from training
  $endgroup$
  – jonnor
  Jun 24 '18 at 11:45
  

  
  
  
  

add a comment | 

0

$begingroup$

So I understand the general idea of how isolation forests works, but I'm having trouble understanding how the model makes predictions on new data.

Does it pass every new point (separately) through every tree in the trained model, and then runs the exact same splits with the exact same original subset data (plus this single new point), to determine the number of steps (anomaly score) until this new point gets isolated?
Then this score gets compared back to the anomaly score threshold that was set when the model was trained?

random-forest decision-trees anomaly-detection

share|improve this question

asked Mar 21 '18 at 22:41

Michael DuMichael Du

11

$endgroup$

bumped to the homepage by Community♦ 43 mins ago

This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.

• 
  
  
  

  
  

  
  
  
  
  
  

  
  $begingroup$
  What do you mean by original subset data? Only the trees are remembered from training
  $endgroup$
  – jonnor
  Jun 24 '18 at 11:45
  

  
  
  
  

add a comment | 

$begingroup$

So I understand the general idea of how isolation forests works, but I'm having trouble understanding how the model makes predictions on new data.

Does it pass every new point (separately) through every tree in the trained model, and then runs the exact same splits with the exact same original subset data (plus this single new point), to determine the number of steps (anomaly score) until this new point gets isolated?
Then this score gets compared back to the anomaly score threshold that was set when the model was trained?

random-forest decision-trees anomaly-detection

share|improve this question

asked Mar 21 '18 at 22:41

Michael DuMichael Du

11

$endgroup$

share|improve this question

asked Mar 21 '18 at 22:41

Michael DuMichael Du

11

share|improve this question

asked Mar 21 '18 at 22:41

Michael DuMichael Du

11

asked Mar 21 '18 at 22:41

Michael DuMichael Du

11

asked Mar 21 '18 at 22:41

Michael DuMichael Du

11

2 Answers
2

active

oldest

votes

0

$begingroup$

You can find the sklearn implementation of Isolation Forest in Python at https://github.com/scikit-learn/scikit-learn/blob/a24c8b46/sklearn/ensemble/iforest.py#L229

It calculates the mean path depth needed to classify new samples. They are scored relatively to a theoretical average path length. Original training data is not used, only the learned trees.

share|improve this answer

answered Jun 24 '18 at 11:52

jonnorjonnor

2726

$endgroup$

add a comment | 

0

$begingroup$

Yes, it pass every new point (separately) through every tree in the trained model, and then runs the exact same splits with the exact same original subset data (plus this single new point), to determine the number of steps (anomaly score) until this new point gets isolated.
Then it will average the path length computed from all the trees for that test instance and this would be the final anomaly score which is then normalized in the range 0-1.

share|improve this answer

answered Mar 13 at 18:30

ShivanyaShivanya

164

$endgroup$

add a comment | 

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0

$begingroup$

You can find the sklearn implementation of Isolation Forest in Python at https://github.com/scikit-learn/scikit-learn/blob/a24c8b46/sklearn/ensemble/iforest.py#L229

It calculates the mean path depth needed to classify new samples. They are scored relatively to a theoretical average path length. Original training data is not used, only the learned trees.

share|improve this answer

answered Jun 24 '18 at 11:52

jonnorjonnor

2726

$endgroup$

add a comment | 

0

$begingroup$

You can find the sklearn implementation of Isolation Forest in Python at https://github.com/scikit-learn/scikit-learn/blob/a24c8b46/sklearn/ensemble/iforest.py#L229

It calculates the mean path depth needed to classify new samples. They are scored relatively to a theoretical average path length. Original training data is not used, only the learned trees.

share|improve this answer

answered Jun 24 '18 at 11:52

jonnorjonnor

2726

$endgroup$

add a comment | 

$begingroup$

You can find the sklearn implementation of Isolation Forest in Python at https://github.com/scikit-learn/scikit-learn/blob/a24c8b46/sklearn/ensemble/iforest.py#L229

It calculates the mean path depth needed to classify new samples. They are scored relatively to a theoretical average path length. Original training data is not used, only the learned trees.

share|improve this answer

answered Jun 24 '18 at 11:52

jonnorjonnor

2726

$endgroup$

share|improve this answer

answered Jun 24 '18 at 11:52

jonnorjonnor

2726

answered Jun 24 '18 at 11:52

jonnorjonnor

2726

answered Jun 24 '18 at 11:52

jonnorjonnor

2726

0

$begingroup$

Yes, it pass every new point (separately) through every tree in the trained model, and then runs the exact same splits with the exact same original subset data (plus this single new point), to determine the number of steps (anomaly score) until this new point gets isolated.
Then it will average the path length computed from all the trees for that test instance and this would be the final anomaly score which is then normalized in the range 0-1.

share|improve this answer

answered Mar 13 at 18:30

ShivanyaShivanya

164

$endgroup$

add a comment | 

0

$begingroup$

Yes, it pass every new point (separately) through every tree in the trained model, and then runs the exact same splits with the exact same original subset data (plus this single new point), to determine the number of steps (anomaly score) until this new point gets isolated.
Then it will average the path length computed from all the trees for that test instance and this would be the final anomaly score which is then normalized in the range 0-1.

share|improve this answer

answered Mar 13 at 18:30

ShivanyaShivanya

164

$endgroup$

add a comment | 

$begingroup$

Yes, it pass every new point (separately) through every tree in the trained model, and then runs the exact same splits with the exact same original subset data (plus this single new point), to determine the number of steps (anomaly score) until this new point gets isolated.
Then it will average the path length computed from all the trees for that test instance and this would be the final anomaly score which is then normalized in the range 0-1.

share|improve this answer

answered Mar 13 at 18:30

ShivanyaShivanya

164

$endgroup$

share|improve this answer

answered Mar 13 at 18:30

ShivanyaShivanya

164

answered Mar 13 at 18:30

ShivanyaShivanya

164

answered Mar 13 at 18:30

ShivanyaShivanya

164