discrimintools.summaryPLSLDA#

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

Printing summaries of Partial Least Squares Linear Discriminant Analysis model.

Parameters:

obj (class) – An object of class PLSLDA.
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.
summarySTEPDISC: Printing summaries of Stepwise Discriminant Analysis model.

Examples

>>> from discrimintools.datasets import load_dataset
>>> from discrimintools import PLSLDA, summaryPLSLDA
>>> D = load_dataset("breast")
>>> y, X = DTrain["Class"], D.drop(columns=["Class"])
>>> clf = PLSLDA()
>>> clf.fit(X,y)
PLSLDA()
>>> summaryPLSLDA(clf)
                    Partial Least Squares Linear Discriminant Analysis - Results
Class Level Information:
          Frequency  Proportion  Prior Probability
negative        458      0.6552             0.6552
positive        241      0.3448             0.3448
Importance of PLS components:
      Proportion (%)  Cumulative (%)
Can1         69.1520         69.1520
Can2         20.1981         89.3501
Raw Canonical and Classification Functions Coefficients:
             Can1    Can2  negative  positive
Constant  -1.6329 -0.1467    1.1027   -7.2713
ucellsize  0.2302 -0.1985   -0.4268    0.8112
normnucl   0.2003  0.0083   -0.2664    0.5063
mitoses    0.2119  0.4687   -0.0150    0.0285

discrimintools.summaryPLSLDA#

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