discrimintools.summaryPLSLOGIT#

discrimintools.summaryPLSLOGIT(obj, digits=4, detailed=False, to_markdown=False, tablefmt='github', **kwargs)[source]#

Printing summaries of Partial Least Squares Logistic Regression model.

Parameters:

  • obj (class) – An object of class PLSLOGIT.

  • digits (int, default = 4) – The number of decimal printed.

  • detailed (bool, default = False) – To print detailed summaries.

  • to_markdown (bool, default = False) – To print summaries in markdown-friendly format. Requires the tabulate. package.

  • tablefmt (str, default = “github”) – The table format.

  • **kwargs – Additionals parameters. These parameters will be passed to tabulate.

Return type:

NoneType

See also

summaryCANDISC

Printing summaries of Canonical Discriminant Analysis model.

summaryCPLS

Printing summaries of Partial Least Squares for Classification model.

summaryDA

Printing summaries of Discriminant Analysis model.

summaryDiCA

Printing summaries of Discriminant Correspondence Analysis model.

summaryDISCRIM

Printing summaries of Discriminant Analysis (linear and quadratic) model.

summaryGFALDA

Printing summaries of General Factor Analysis Linear Discriminant Analysis model.

summaryMDA

Printing summaries of Mixed Discriminant Analysis model.

summaryPLSDA

Printing summaries of Partial Least Squares Discriminant Analysis model.

summaryPLSLDA

Printing summaries of Partial Least Squares Linear Discriminant Analysis model.

summarySTEPDISC

Printing summaries of Stepwise Discriminant Analysis model.

Examples

>>> from discrimintools.datasets import load_dataset
>>> from discrimintools import PLSLDA, summaryPLSLOGIT
>>> D = load_dataset("breast")
>>> y, X = DTrain["Class"], D.drop(columns=["Class"])
>>> clf = PLSLOGIT()
>>> clf.fit(X,y)
PLSLOGIT()
>>> summaryPLSLOGIT(clf)
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