Questions tagged [isolation-forest]

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How to validate an Isolation Forest for unsupervised outlier detection

Im currently building a binary classification model for outlier detection using isolation forest. I predicted some outliers and now i should validate my model. Im most unsupervised examples that ive ...
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Can GridSearchCV be used for unsupervised learning?

im trying to build an outlier detector to find outliers in test data. That data varies a bit (more test channels, longer/shorter testing). First im applying the train test split because i want to use ...
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Handling features dependent on a data field that may not be present in sample

I'm trying to build an anomaly detection model using Isolation Forest. I currently have 12 features, about half of them depends on the presence of a particular data field, say ...
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Why anomaly detection with IForest does not give expected results?

My goal is to develop an anomaly detection model with Isolation Forest in order to distinguish between normal and anomalous IPs by analyzing the web access logs and ...
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Interpreting SHAP values from Isolation Forest model

I have a very similar question to the one asked a year ago. I have an Isolation Forest model written in a very similar way as the question I linked, and my ...
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Understanding Isolation Forest predictions

I'm running sklearn's IsolationForest on a dataset containing 2 classes of data, one that I know is the anomaly (~1.5% of the entire dataset), the other is the normal dataset. I'm using this (shuffled)...
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Who to make IsolationForest more sensitive to single-feature outliers?

I am using IsolationForest with pycaret. I find that the algorithm identifies instances where many features are somewhat different, but when most features are ...
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251 views

Evaluate multiple Isolation Forest estimators during GridSearchCV with custom scorer function

I have a sample of values that don't have a y target value. Actually, the X features (predictors) are all used to fit the Isolation Forest estimator. The goal is to identify which of those X-features ...
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Anomaly detection and root cause analysis

ARIMA is widely used for anomaly detection on time-series data e.g. stock price prediction. ARIMA assumes that future value of a variable (stock price in our case) is dependent on its previous values. ...
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Can I run isolation forest on existing data to find anomalies, save it for the future and use it on incoming data?

One of the major arguments I had recently is if we can save an unsupervised learning model to disk and use it later on incoming data. Isolation forest is one of the models that I use a lot for ...
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1 answer
56 views

Word2vec to encode medical procedures when using isolation forests

I am planning to use Isolation Forests in R (solitude package) to identify outlier medical claims in my data. Each row of my data represents the group of drugs that each provider has administered in ...
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Isolation Forest in R using Solitude - From the results how can I identify the anomalous records

I am trying to use the Isolation Forest algorithm in the Solitude package to identify anomalous rows in my data. I'm using the examples in the documentation to learn about the algorithm, this example ...
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How do I determine the top "reason" for anomaly when using Isolation Forests

I am using Isolation Forests for Anomaly Detection. Say, my set has 10 variables, var1, var2, ..., var10, and I found an anomaly. Can I rank the 10 variables var1, var2, ..., var10 in such a way I can ...
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622 views

Anomaly (Outlier) Detection with Isolation Forest too sensitive even with low contamination

I'm trying to use the sklearn implementation of the Isolation Forest algorithm to detect anomalies in my time series data. However, even with a very low contamination parameter (0.0001), it is ...
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1 answer
191 views

Calculating accuracy score of isolation forest model returning error

My code is as follows: ...
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1 answer
427 views

The affect of bootstrap on Isolation Forest

I've been using isolation forest for anomaly detection, and reviewing its parameters at scikit-learn (link). Looking at "bootstrap", I'm not quite clear what using bootstrap would cause. For ...
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Anomaly Detection

I have a problem where I want to identify Vendors with unusual high amount invoices. What would be the best way to identify such invoices? I am trying to use Isolation Forest but having trouble in ...
3 votes
1 answer
149 views

Cross-Validation for Unsupervised Anomaly Detection with Isolation Forest

I am wondering whether I can perform any kind of Cross-Validation or GridSearchCV for unsupervised learning. The thing is that I have the ground truth labels (but since it is unsupervised I just drop ...
3 votes
1 answer
148 views

Geolocation Based Anomaly Detection in IPs Using Isolation Forest

I'm trying to detect anomalies based on geolocation from IP addresses on a server access log file. I have created two features country and geo_velocity, using the IP address and the timestamp of each ...
2 votes
0 answers
487 views

How SHAP value explains contribution of features for outliers event?

I'm trying to understand and experiment with how the SHAP value can explain behaviour for each outlier events (rows) and how it can be related to shap.force_plot(). ...
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1 answer
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Why do Isolation Forest implementations turn it into a supervised learning problem (with random values for the target?)

I am looking at various implementations of the Isolation Forest in python and R. Both sklearn in python and solitude in R use a y variable with the ExtraTrees regressor. Since, Isolation Forest is ...
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2 answers
82 views

Incorrect multi-variate anomaly detection - Isolation Forest Python

My data looks like below. it has 333 rows and 2 columns. Clearly the first row is anomaly. ndf: ...
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2 answers
726 views

Cross-Validation in Anomaly Detection with Labelled Data

I am working on a project where I train anomaly detection algorithms Isolation Forest and Auto-Encoder. My data is labelled so I have the ground truth but the nature of the problem requires ...
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190 views

Anomaly Detection over multivariate data containing Nominal and numerical predictors

I am trying to implement Anomaly Detection over a multivariate dataset having nominal and numerical predictors. Dataset has following pattern: If we consider the below sample records, category_id, ...
1 vote
2 answers
100 views

Identify the parameter causing the anomaly in a multivariate dataset

I have a payment transaction dataset with a large number of predictor variables. I am trying to build a model for anomaly detection and I have evaluated various algorithms/approaches for the same like ...
5 votes
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2k views

Dealing with categorical variables in Isolation Forest

Isolation Forest is widely used when dealing with outlier/anomaly detection when we have no labels. The theory behind is that making random split at random points and counting how many splits you do ...
1 vote
1 answer
149 views

Isolation Forest: simple example

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 ...