Questions tagged [isolation-forest]
The isolation-forest tag has no usage guidance.
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Varying feature vector lengths for learning
[I am a total beginner in machine learning algorithms]
I have 10 spectrograms (lines) for phytoplankton (each composed of 288 points).
Each spectrogram is associated with a phytoplankton dendity data ...
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How to apply a tree-based model with numerical and categorical values to find outliers
I have a dataset which has a column of prices, a column of dates, and various other columns of numerical and categorical values. I would like to find outlier prices based on all the columns in the ...
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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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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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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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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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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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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 ...
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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 ...
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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 ...
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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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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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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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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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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, ...
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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 ...
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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 ...
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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 ...
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