discrimintools.summaryDISCRIM

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

Printing summaries of Discriminant Analysis (linear and quadratic) model.

Parameters:

• obj (class) – an object of class DISCRIM.

• 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.

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_alcools
>>> from discrimintools import DISCRIM, summaryDISCRIM
>>> D = load_alcools()
>>> y, X = D["TYPE"], D.drop(columns=["TYPE"])
>>> clf = DISCRIM()
>>> clf.fit(X,y)
DISCRIM(priors='prop')
>>> summaryDISCRIM(clf)
                    Discriminant Analysis - Results
Summary Information:
               Infos  Value                  DF  DF value
0  Total Sample Size     52            DF Total        51
1          Variables      8   DF Within Classes        49
2            Classes      3  DF Between Classes         2
Class Level Information:
        Frequency  Proportion  Prior Probability
KIRSCH         17      0.3269             0.3269
MIRAB          15      0.2885             0.2885
POIRE          20      0.3846             0.3846
Linear Discriminant Function for TYPE:
          KIRSCH    MIRAB    POIRE
Constant -5.0165 -18.8407 -24.7649
MEOH      0.0034   0.0290   0.0334
ACET      0.0064   0.0164   0.0075
BU1      -0.0637   0.4054   0.3180
BU2      -0.0009   0.0714   0.1150
ISOP      0.0231   0.0298  -0.0085
MEPR      0.0375  -0.1289   0.0618
PRO1      0.0020  -0.0054  -0.0083
ACAL      0.0662  -0.2264  -0.1303