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Isolation Forest

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?

data-science-model outlier

share|improve this question

edited Apr 2 at 3:42

Stephen Rauch♦

1,52551330

asked Apr 2 at 2:49

Shyam KishorShyam Kishor

1

$endgroup$

add a comment | 

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?

data-science-model outlier

share|improve this question

edited Apr 2 at 3:42

Stephen Rauch♦

1,52551330

asked Apr 2 at 2:49

Shyam KishorShyam Kishor

1

$endgroup$

add a comment | 

$begingroup$

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?

data-science-model outlier

share|improve this question

edited Apr 2 at 3:42

Stephen Rauch♦

1,52551330

asked Apr 2 at 2:49

Shyam KishorShyam Kishor

1

$endgroup$

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

share|improve this question

edited Apr 2 at 3:42

Stephen Rauch♦

1,52551330

asked Apr 2 at 2:49

Shyam KishorShyam Kishor

1

share|improve this question

edited Apr 2 at 3:42

Stephen Rauch♦

1,52551330

asked Apr 2 at 2:49

Shyam KishorShyam Kishor

1

edited Apr 2 at 3:42

Stephen Rauch♦

1,52551330

edited Apr 2 at 3:42

Stephen Rauch♦

1,52551330

asked Apr 2 at 2:49

Shyam KishorShyam Kishor

1

asked Apr 2 at 2:49

Shyam KishorShyam Kishor

1

1 Answer
1

active

oldest

votes

1

$begingroup$

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:

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

edited Apr 2 at 3:43

answered Apr 2 at 3:38

EthanEthan

700625

$endgroup$

add a comment | 

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1

$begingroup$

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:

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

edited Apr 2 at 3:43

answered Apr 2 at 3:38

EthanEthan

700625

$endgroup$

add a comment | 

1

$begingroup$

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:

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

edited Apr 2 at 3:43

answered Apr 2 at 3:38

EthanEthan

700625

$endgroup$

add a comment | 

$begingroup$

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:

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

edited Apr 2 at 3:43

answered Apr 2 at 3:38

EthanEthan

700625

$endgroup$

share|improve this answer

edited Apr 2 at 3:43

answered Apr 2 at 3:38

EthanEthan

700625

answered Apr 2 at 3:38

EthanEthan

700625

answered Apr 2 at 3:38

EthanEthan

700625