discrimintools.summarySTEPDISC#

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

Printing summaries of Stepwise Discriminant Analysis model.

Parameters:

obj (class) – An object of class STEPDISC.
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.

Examples

>>> from discrimintools.datasets import load_dataset
>>> from discrimintools import DISCRIM, STEPDISC, summarySTEPDISC
>>> D = load_dataset("breast")
>>> y, X = D["Class"], D.drop(columns=["Class"])
>>> clf = DISCRIM()
>>> clf.fit(X,y)
DISCRIM(priors='prop')
>>> clf2 = STEPDISC(method="backward",alpha=0.01,verbose=False)
>>> clf2.fit(clf)
STEPDISC()
>>> summarySTEPDISC(clf2)
                    Stepwise Discriminant Analysis - Results
====================== Before backward selection  =======================
                    Discriminant Analysis - Results
Summary Information:
               Infos  Value                  DF  DF value
0  Total Sample Size    150            DF Total       149
1          Variables     18   DF Within Classes       148
2            Classes      2  DF Between Classes         1
Class Level Information:
          Frequency  Proportion  Prior Probability
absence          82      0.5467             0.5467
presence         68      0.4533             0.4533
Linear Discriminant Function for disease:
                            absence  presence
Constant                  -124.3546 -127.3863
age                          1.1836    1.1914
sexmale                     14.2659   15.9123
chestpainatypicalAngina      0.5668   -1.8839
chestpainnonAnginal          3.4872    1.6652
...
slopeflat                   18.6429   19.8215
slopeupsloping              14.7467   15.2526
vesselsColored              -2.5087   -1.0308
thalnormal                  21.1525   20.1211
thalreversableEffect        14.8540   16.1636
====================== After backward selection  =======================
                    Discriminant Analysis - Results
Summary Information:
               Infos  Value                  DF  DF value
0  Total Sample Size    150            DF Total       149
1          Variables      7   DF Within Classes       148
2            Classes      2  DF Between Classes         1
Class Level Information:
          Frequency  Proportion  Prior Probability
absence          82      0.5467             0.5467
presence         68      0.4533             0.4533
Linear Discriminant Function for disease:
                         absence  presence
Constant                 -3.5002   -6.2648
sexmale                   3.0078    4.6211
chestpainatypicalAngina   4.6553    1.9352
chestpainnonAnginal       4.6453    2.1583
chestpaintypicalAngina    2.6881   -2.0794
oldpeak                   0.7868    1.8112
vesselsColored            0.7138    2.0291
thalreversableEffect      0.6085    2.4439

discrimintools.summarySTEPDISC#

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