Isolation Forest Prediction Mechanics: Does it compare value with every tree (and the original training...
$begingroup$
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
asked Mar 21 '18 at 22:41
Michael DuMichael Du
11
$endgroup$
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
asked Mar 21 '18 at 22:41
Michael DuMichael Du
11
$endgroup$
$begingroup$
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
asked Mar 21 '18 at 22:41
Michael DuMichael Du
11
$endgroup$
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
random-forest decision-trees anomaly-detection
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
asked Mar 21 '18 at 22:41
Michael DuMichael Du
11
asked Mar 21 '18 at 22:41
Michael DuMichael Du
11
11
$begingroup$
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 |
$begingroup$
What do you mean by original subset data? Only the trees are remembered from training
$endgroup$
– jonnor
Jun 24 '18 at 11:45
$begingroup$
What do you mean by original subset data? Only the trees are remembered from training
$endgroup$
– jonnor
Jun 24 '18 at 11:45
$begingroup$
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 |
2 Answers
2
active
oldest
votes
$begingroup$
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.
answered Jun 24 '18 at 11:52
jonnorjonnor
2175
$endgroup$
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.
answered 2 days ago
ShivanyaShivanya
164
New contributor
Shivanya 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 |
Your Answer
StackExchange.ifUsing("editor", function () {
return StackExchange.using("mathjaxEditing", function () {
StackExchange.MarkdownEditor.creationCallbacks.add(function (editor, postfix) {
StackExchange.mathjaxEditing.prepareWmdForMathJax(editor, postfix, [["$", "$"], ["\\(","\\)"]]);
});
});
}, "mathjax-editing");
StackExchange.ready(function() {
var channelOptions = {
tags: "".split(" "),
id: "557"
};
initTagRenderer("".split(" "), "".split(" "), channelOptions);
StackExchange.using("externalEditor", function() {
// Have to fire editor after snippets, if snippets enabled
if (StackExchange.settings.snippets.snippetsEnabled) {
StackExchange.using("snippets", function() {
createEditor();
});
}
else {
createEditor();
}
});
function createEditor() {
StackExchange.prepareEditor({
heartbeatType: 'answer',
autoActivateHeartbeat: false,
convertImagesToLinks: false,
noModals: true,
showLowRepImageUploadWarning: true,
reputationToPostImages: null,
bindNavPrevention: true,
postfix: "",
imageUploader: {
brandingHtml: "Powered by u003ca class="icon-imgur-white" href="https://imgur.com/"u003eu003c/au003e",
contentPolicyHtml: "User contributions licensed under u003ca href="https://creativecommons.org/licenses/by-sa/3.0/"u003ecc by-sa 3.0 with attribution requiredu003c/au003e u003ca href="https://stackoverflow.com/legal/content-policy"u003e(content policy)u003c/au003e",
allowUrls: true
},
onDemand: true,
discardSelector: ".discard-answer"
,immediatelyShowMarkdownHelp:true
});
}
});
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
StackExchange.ready(
function () {
StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fdatascience.stackexchange.com%2fquestions%2f29398%2fisolation-forest-prediction-mechanics-does-it-compare-value-with-every-tree-an%23new-answer', 'question_page');
}
);
Post as a guest
Required, but never shown
2 Answers
2
active
oldest
votes
2 Answers
2
active
oldest
votes
active
oldest
votes
active
oldest
votes
$begingroup$
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.
answered Jun 24 '18 at 11:52
jonnorjonnor
2175
$endgroup$
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.
answered Jun 24 '18 at 11:52
jonnorjonnor
2175
$endgroup$
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.
answered Jun 24 '18 at 11:52
jonnorjonnor
2175
$endgroup$
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.
answered Jun 24 '18 at 11:52
jonnorjonnor
2175
answered Jun 24 '18 at 11:52
jonnorjonnor
2175
answered Jun 24 '18 at 11:52
jonnorjonnor
2175
answered Jun 24 '18 at 11:52
jonnorjonnor
2175
2175
add a comment |
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.
answered 2 days ago
ShivanyaShivanya
164
New contributor
Shivanya 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 |
$begingroup$
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.
answered 2 days ago
ShivanyaShivanya
164
New contributor
Shivanya 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 |
$begingroup$
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.
answered 2 days ago
ShivanyaShivanya
164
New contributor
Shivanya is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
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.
answered 2 days ago
ShivanyaShivanya
164
New contributor
Shivanya is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
answered 2 days ago
ShivanyaShivanya
164
New contributor
Shivanya is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
answered 2 days ago
ShivanyaShivanya
164
answered 2 days ago
ShivanyaShivanya
164
164
New contributor
Shivanya is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
Shivanya is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
Shivanya 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 |
Thanks for contributing an answer to Data Science Stack Exchange!
- Please be sure to answer the question. Provide details and share your research!
But avoid …
- Asking for help, clarification, or responding to other answers.
- Making statements based on opinion; back them up with references or personal experience.
Use MathJax to format equations. MathJax reference.
To learn more, see our tips on writing great answers.
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
StackExchange.ready(
function () {
StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fdatascience.stackexchange.com%2fquestions%2f29398%2fisolation-forest-prediction-mechanics-does-it-compare-value-with-every-tree-an%23new-answer', 'question_page');
}
);
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
$begingroup$
What do you mean by original subset data? Only the trees are remembered from training
$endgroup$
– jonnor
Jun 24 '18 at 11:45