discrimintools.summaryDiCA

discrimintools.summaryDiCA(obj, digits=4, detailed=False, to_markdown=False, tablefmt='github', **kwargs)

Printing summaries of Discriminant Correspondence Analysis model.

Parameters:

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

• 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.

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_divay
>>> from discrimintools import DiCA, summaryDiCA
>>> D = load_divay() # load training data
>>> y, X = D["Region"], D.drop(columns=["Region"]) # split into X and y
>>> clf = DiCA()
>>> clf.fit(X,y)
DiCA()
>>> summaryDiCA(clf)
                    Discriminant Correspondence Analysis - Results
Class Level Information:
            Frequency  Proportion  Prior Probability
Beaujolais          4      0.3333             0.3333
Loire               4      0.3333             0.3333
Rhone               4      0.3333             0.3333
Importance of components:
      Eigenvalue  Difference  Proportion (%)  Cumulative (%)
Can1      0.2519      0.0504         55.5635         55.5635
Can2      0.2014         NaN         44.4365        100.0000
Canonical correlation:
      Eigenvalue  Total SS  Eta Sq.  Canonical Correlation
Can1      0.2519    4.0879   0.7394                 0.8599
Can2      0.2014    3.4864   0.6934                 0.8327
Classification (projection) coefficients:
             Can1    Can2
Woody_A   -0.3724 -0.0188
Woody_B    0.0204  0.1559
Woody_C    0.3520 -0.1371
Fruity_A   0.3520 -0.1371
Fruity_B   0.0204  0.1559
Fruity_C  -0.3724 -0.0188
Sweet_A    0.2017  0.2855
Sweet_B   -0.1309 -0.0582
Sweet_C   -0.0163 -0.1247
Alcohol_A  0.0558 -0.3276
Alcohol_B -0.1309 -0.0582
Alcohol_C  0.0815  0.6236
Hedonic_A -0.0000 -0.0000
Hedonic_B -0.0000 -0.0000
Hedonic_C -0.0000 -0.0000
Hedonic_D -0.0000 -0.0000