discrimintools.summaryCPLS#

discrimintools.summaryCPLS(obj, ncp=2, digits=4, detailed=False, to_markdown=False, tablefmt='github', **kwargs)[source]#

Printing summaries of Partial Least Squares for Classification model.

Parameters:

obj (class) – An object of class CPLS.
ncp (int,, default = 2) – Number of pls components.
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.
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 CPLS, summaryCPLS
>>> DTrain = load_dataset("breast") # load training data
>>> y, X = D["Class"], D.drop(columns=["Class"]) # split into X and y
>>> clf = CPLS()
>>> clf.fit(X,y)
CPLS()
>>> summaryCPLS(clf)
                        Partial Least Squares for Classification - Results
Class Level Information:
          Frequency  Proportion  Prior Probability
negative        458      0.6552             0.6552
positive        241      0.3448             0.3448
Classification functions coefficients:
           positive       VIP
Constant  -0.424881       NaN
ucellsize  0.085323  1.203759
normnucl   0.053251  1.034559
mitoses    0.003001  0.693292

discrimintools.summaryCPLS#

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