discrimintools.eval_predict

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

Evaluation of the prediction’ quality on training dataset.

Parameters:

• obj (class) – an object of class CANDISC, CPLS, DiCA, DISCRIM, GFALDA, MDA, PLSDA or PLSLDA.

• digits (int, default = 4) – The number of decimal printed.

• 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.

summarySTEPDISC

Printing summaries of Stepwise Discriminant Analysis model.

Examples

>>> from discrimintools.datasets import load_heart
>>> from discrimintools import DISCRIM, eval_predict
>>> D = load_heart() # load training data
>>> y, X = D["disease"], D.drop(columns=["disease"]) # split into X and y
>>> clf = DISCRIM()
>>> clf.fit(X,y)
Categorical features have been encoded into binary variables.
DISCRIM(priors='prop')
>>> eval_predict(clf)
Classification Summary for Calibration Data:
Observation Profile:
                        Read  Used
Number of Observations   150   150
Number of Observations Classified into disease:
prediction  absence  presence  Total
disease
absence          75         7     82
presence         12        56     68
Total            87        63    150
Percent Classified into disease:
prediction  absence  presence  Total
disease
absence     91.4634    8.5366  100.0
presence    17.6471   82.3529  100.0
Total       58.0000   42.0000  100.0
Priors       0.5467    0.4533    NaN
Error Count Estimates for disease:
        absence  presence   Total
Rate     0.0854    0.1765  0.1267
Priors   0.5467    0.4533     NaN
Classification Report for disease:
              precision  recall  f1-score   support
absence          0.8621  0.9146    0.8876   82.0000
presence         0.8889  0.8235    0.8550   68.0000
accuracy         0.8733  0.8733    0.8733    0.8733
macro avg        0.8755  0.8691    0.8713  150.0000
weighted avg     0.8742  0.8733    0.8728  150.0000