pycaret.clustering.create_model#

pycaret.clustering.create_model(model: str | Any, num_clusters: int = 4, ground_truth: str | None = None, round: int = 4, fit_kwargs: dict | None = None, verbose: bool = True, experiment_custom_tags: Dict[str, Any] | None = None, engine: str | None = None, **kwargs)[source]#

This function trains and evaluates the performance of a given model. Metrics evaluated can be accessed using the get_metrics function. Custom metrics can be added or removed using the add_metric and remove_metric function. All the available models can be accessed using the models function.

Example

>>> from pycaret.datasets import get_data
>>> jewellery = get_data('jewellery')
>>> from pycaret.clustering import *
>>> exp_name = setup(data = jewellery)
>>> kmeans = create_model('kmeans')
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. Models available in the model library (ID - Name):

  • ‘kmeans’ - K-Means Clustering

  • ‘ap’ - Affinity Propagation

  • ‘meanshift’ - Mean shift Clustering

  • ‘sc’ - Spectral Clustering

  • ‘hclust’ - Agglomerative Clustering

  • ‘dbscan’ - Density-Based Spatial Clustering

  • ‘optics’ - OPTICS Clustering

  • ‘birch’ - Birch Clustering

  • ‘kmodes’ - K-Modes Clustering

num_clusters: int, default = 4

The number of clusters to form.

ground_truth: str, default = None

ground_truth to be provided to evaluate metrics that require true labels. When None, such metrics are returned as 0.0. All metrics evaluated can be accessed using get_metrics function.

round: int, default = 4

Number of decimal places the metrics in the score grid will be rounded to.

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

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

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.

engine: Optional[str] = None

The execution engine to use for the model, e.g. for K-Means Clustering (“kmeans”), users can switch between “sklearn” and “sklearnex” by specifying engine=”sklearnex”.

**kwargs:

Additional keyword arguments to pass to the estimator.

Returns:

Trained Model

Warning

  • num_clusters param not required for Affinity Propagation (‘ap’), Mean shift (‘meanshift’), Density-Based Spatial Clustering (‘dbscan’) and OPTICS Clustering (‘optics’).

  • When fit doesn’t converge in Affinity Propagation (‘ap’) model, all datapoints are labelled as -1.

  • Noisy samples are given the label -1, when using Density-Based Spatial (‘dbscan’) or OPTICS Clustering (‘optics’).

  • OPTICS (‘optics’) clustering may take longer training times on large datasets.

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