pycaret.anomaly.create_model#

pycaret.anomaly.create_model(model: str | Any, fraction: float = 0.05, verbose: bool = True, fit_kwargs: dict | None = None, experiment_custom_tags: Dict[str, Any] | None = None, **kwargs)[source]#

This function trains a given model from the model library. All available models can be accessed using the models function.

Example

>>> from pycaret.datasets import get_data
>>> anomaly = get_data('anomaly')
>>> from pycaret.anomaly import *
>>> exp_name = setup(data = anomaly)
>>> knn = create_model('knn')
model: str or scikit-learn compatible object

ID of an model available in the model library or pass an untrained model object consistent with scikit-learn API. Estimators available in the model library (ID - Name):

  • ‘abod’ - Angle-base Outlier Detection

  • ‘cluster’ - Clustering-Based Local Outlier

  • ‘cof’ - Connectivity-Based Outlier Factor

  • ‘histogram’ - Histogram-based Outlier Detection

  • ‘iforest’ - Isolation Forest

  • ‘knn’ - k-Nearest Neighbors Detector

  • ‘lof’ - Local Outlier Factor

  • ‘svm’ - One-class SVM detector

  • ‘pca’ - Principal Component Analysis

  • ‘mcd’ - Minimum Covariance Determinant

  • ‘sod’ - Subspace Outlier Detection

  • ‘sos’ - Stochastic Outlier Selection

fraction: float, default = 0.05

The amount of contamination of the data set, i.e. the proportion of outliers in the data set. Used when fitting to define the threshold on the decision function.

verbose: bool, default = True

Status update is not printed when verbose is set to False.

experiment_custom_tags: dict, default = None

Dictionary of tag_name: String -> value: (String, but will be string-ified if not) passed to the mlflow.set_tags to add new custom tags for the experiment.

fit_kwargs: dict, default = {} (empty dict)

Dictionary of arguments passed to the fit method of the model.

**kwargs:

Additional keyword arguments to pass to the estimator.

Returns:

Trained Model

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