anomaly-detection / README.md
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metadata
license: mit
library_name: sklearn
tags:
  - sklearn
  - skops
  - tabular-classification
model_format: pickle
model_file: isolation_forest.pkl
widget:
  structuredData:
    x0:
      - 1.9137876638235471
      - -1.8264435506813366
      - -2.1884262678924737
    x1:
      - 2.021017965584703
      - -1.895103662902048
      - -2.1443081355382363

Model description

[More Information Needed]

Intended uses & limitations

This model is not ready to be used in production.

Training Procedure

[More Information Needed]

Hyperparameters

Click to expand
Hyperparameter Value
memory
steps [('scaler', StandardScaler()), ('model', IsolationForest(max_samples=100, random_state=0))]
verbose False
scaler StandardScaler()
model IsolationForest(max_samples=100, random_state=0)
scaler__copy True
scaler__with_mean True
scaler__with_std True
model__bootstrap False
model__contamination auto
model__max_features 1.0
model__max_samples 100
model__n_estimators 100
model__n_jobs
model__random_state 0
model__verbose 0
model__warm_start False

Model Plot

Pipeline(steps=[('scaler', StandardScaler()),('model', IsolationForest(max_samples=100, random_state=0))])
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.

Evaluation Results

[More Information Needed]

How to Get Started with the Model

[More Information Needed]

Model Card Authors

This model card is written by following authors:

[More Information Needed]

Model Card Contact

You can contact the model card authors through following channels: [More Information Needed]

Citation

Below you can find information related to citation.

BibTeX:

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