discrimintools.summaryCANDISC#

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

Printing summaries of Canonical Discriminant Analysis model.

Parameters:

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

  • 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

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_wine
>>> from discrimintools import CANDISC, summaryCANDISC
>>> D = load_wine() # load training data
>>> y, X = D["Quality"], D.drop(columns=["Quality"]) # split into X and y
>>> clf = CANDISC()
>>> clf.fit(X,y)
CANDISC()
>>> summaryCANDISC(clf)
                        Canonical Discriminant Analysis - Results
Summary Information:
               infos  Value                  DF  DF value
0  Total Sample Size     34            DF Total        33
1          Variables      4   DF Within Classes        31
2            Classes      3  DF Between Classes         2
Class Level Information:
          Frequency  Proportion  Prior Probability
Mediocre         12      0.3529             0.3529
Moyen            11      0.3235             0.3235
Bon              11      0.3235             0.3235
Total-Sample Class Means:
              Mediocre      Moyen        Bon
Temperature  3037.3333  3140.9091  3306.3636
Soleil       1126.4167  1262.9091  1363.6364
Chaleur        12.0833    16.4545    28.5455
Pluie         430.3333   339.6364   305.0000
Importance of components:
      Eigenvalue  Difference  Proportion  Cumulative
Can1      3.2789      3.1403     95.9451     95.9451
Can2      0.1386         NaN      4.0549    100.0000
Raw Canonical and Classification Functions Coefficients:
                Can1    Can2  Mediocre   Moyen      Bon
Constant    -32.8763  2.1653   65.6093 -7.1918 -72.5905
Temperature   0.0086 -0.0000   -0.0178  0.0013   0.0182
Soleil        0.0068 -0.0053   -0.0153  0.0037   0.0129
Chaleur      -0.0271  0.1276    0.0845 -0.0694  -0.0227
Pluie        -0.0059  0.0062    0.0136 -0.0040  -0.0108
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