Isolation Forest
$begingroup$
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
edited 10 mins ago
Stephen Rauch
1,52551330
asked 1 hour ago
Shyam KishorShyam Kishor
1
New contributor
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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
edited 10 mins ago
Stephen Rauch
1,52551330
asked 1 hour ago
Shyam KishorShyam Kishor
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.
$endgroup$
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
edited 10 mins ago
Stephen Rauch
1,52551330
asked 1 hour ago
Shyam KishorShyam Kishor
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.
$endgroup$
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
data-science-model outlier
edited 10 mins ago
Stephen Rauch
1,52551330
asked 1 hour ago
Shyam KishorShyam Kishor
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.
edited 10 mins ago
Stephen Rauch
1,52551330
asked 1 hour ago
Shyam KishorShyam Kishor
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.
edited 10 mins ago
Stephen Rauch
1,52551330
edited 10 mins ago
Stephen Rauch
1,52551330
edited 10 mins ago
Stephen Rauch
1,52551330
1,52551330
asked 1 hour ago
Shyam KishorShyam Kishor
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.
asked 1 hour ago
Shyam KishorShyam Kishor
1
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.
add a comment |
add a comment |
1 Answer
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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)
answered 15 mins ago
EthanEthan
622424
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1 Answer
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1 Answer
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$begingroup$
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)
answered 15 mins ago
EthanEthan
622424
$endgroup$
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)
answered 15 mins ago
EthanEthan
622424
$endgroup$
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)
answered 15 mins ago
EthanEthan
622424
$endgroup$
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:
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)
answered 15 mins ago
EthanEthan
622424
edited 9 mins ago
answered 15 mins ago
EthanEthan
622424
answered 15 mins ago
EthanEthan
622424
answered 15 mins ago
EthanEthan
622424
622424
add a comment |
add a comment |
Shyam Kishor is a new contributor. Be nice, and check out our Code of Conduct.
Shyam Kishor is a new contributor. Be nice, and check out our Code of Conduct.
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