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verticapy.machine_learning.vertica.ensemble.XGBClassifier.report

XGBClassifier.report(metrics: None | str | list[Literal['aic', 'bic', 'accuracy', 'acc', 'balanced_accuracy', 'ba', 'auc', 'roc_auc', 'prc_auc', 'best_cutoff', 'best_threshold', 'false_discovery_rate', 'fdr', 'false_omission_rate', 'for', 'false_negative_rate', 'fnr', 'false_positive_rate', 'fpr', 'recall', 'tpr', 'precision', 'ppv', 'specificity', 'tnr', 'negative_predictive_value', 'npv', 'negative_likelihood_ratio', 'lr-', 'positive_likelihood_ratio', 'lr+', 'diagnostic_odds_ratio', 'dor', 'log_loss', 'logloss', 'f1', 'f1_score', 'mcc', 'bm', 'informedness', 'mk', 'markedness', 'ts', 'csi', 'critical_success_index', 'fowlkes_mallows_index', 'fm', 'prevalence_threshold', 'pm', 'confusion_matrix', 'classification_report']] = None, cutoff: Annotated[int | float | Decimal, 'Python Numbers'] | None = None, labels: None | str | list[str] = None, nbins: int = 10000) → float | TableSample

Computes a classification report using multiple model evaluation metrics (auc, accuracy, f1…). For multiclass classification, it considers each category as positive and switches to the next one during the computation.

Parameters

metrics: list, optional

List of the metrics used to compute the final report.

• accuracy:

  Accuracy.

  \[Accuracy = \frac{TP + TN}{TP + TN + FP + FN}\]

• aic:

  Akaike’s Information Criterion

  \[AIC = 2k - 2\ln(\hat{L})\]

• auc:

  Area Under the Curve (ROC).

  \[AUC = \int_{0}^{1} TPR(FPR) \, dFPR\]

• ba:

  Balanced Accuracy.

  \[BA = \frac{TPR + TNR}{2}\]

• best_cutoff:

  Cutoff which optimised the ROC Curve prediction.

• bic:

  Bayesian Information Criterion

  \[BIC = -2\ln(\hat{L}) + k \ln(n)\]

• bm:

  Informedness

  \[BM = TPR + TNR - 1\]

• csi:

  Critical Success Index

  \[index = \frac{TP}{TP + FN + FP}\]

• f1:

  F1 Score

  \[F_1 Score = 2 \times \frac{Precision \times Recall}{Precision + Recall}\]

• fdr:

  False Discovery Rate

  \[FDR = 1 - PPV\]

• fm:

  Fowlkes-Mallows index

  \[FM = \sqrt{PPV * TPR}\]

• fnr:

  False Negative Rate

  \[FNR = \frac{FN}{FN + TP}\]

• for:

  False Omission Rate

  \[FOR = 1 - NPV\]

• fpr:

  False Positive Rate

  \[FPR = \frac{FP}{FP + TN}\]

• logloss:

  Log Loss.

  \[Loss = -\frac{1}{N} \sum_{i=1}^{N} \left( y_i \log(p_i) + (1 - y_i) \log(1 - p_i) \right)\]

• lr+:

  Positive Likelihood Ratio.

  \[LR+ = \frac{TPR}{FPR}\]

• lr-:

  Negative Likelihood Ratio.

  \[LR- = \frac{FNR}{TNR}\]

• dor:

  Diagnostic Odds Ratio.

  \[DOR = \frac{TP \times TN}{FP \times FN}\]

• mc:

  Matthews Correlation Coefficient .. math:

  MCC = \frac{TP \times TN - FP \times FN}{\sqrt{(TP + FP)(TP + FN)(TN + FP)(TN + FN)}}
  

• mk:

  Markedness

  \[MK = PPV + NPV - 1\]

• npv:

  Negative Predictive Value

  \[NPV = \frac{TN}{TN + FN}\]

• prc_auc:

  Area Under the Curve (PRC)

  \[AUC = \int_{0}^{1} Precision(Recall) \, dRecall\]

• precision:

  Precision

  \[Precision = TP / (TP + FP)\]

• pt:

  Prevalence Threshold.

  \[threshold = \frac{\sqrt{FPR}}{\sqrt{TPR} + \sqrt{FPR}}\]

• recall:

  Recall.

  \[Recall = \frac{TP}{TP + FN}\]

• specificity:

  Specificity.

  \[Specificity = \frac{TN}{TN + FP}\]

cutoff: PythonNumber, optional

Cutoff for which the tested category is accepted as a prediction. For multiclass classification, each tested category becomes the positives and the others are merged into the negatives. The cutoff represents the classes threshold. If it is empty, the regular cutoff (1 / number of classes) is used.

labels: str | list, optional

List of the different labels to be used during the computation.

nbins: int, optional

[Used to compute ROC AUC, PRC AUC and the best cutoff] An integer value that determines the number of decision boundaries. Decision boundaries are set at equally spaced intervals between 0 and 1, inclusive. Greater values for nbins give more precise estimations of the metrics, but can potentially decrease performance. The maximum value is 999,999. If negative, the maximum value is used.

Returns

TableSample

report.

Examples

For this example, we will use the Iris dataset.

import verticapy.datasets as vpd

data = vpd.load_iris()

train, test = data.train_test_split(test_size = 0.2)

	123 SepalLengthCm Numeric(7)	123 SepalWidthCm Numeric(7)	123 PetalLengthCm Numeric(7)	123 PetalWidthCm Numeric(7)	Abc Species Varchar(30)
1	4.6	3.6	1.0	0.2	Iris-setosa
2	4.7	3.2	1.3	0.2	Iris-setosa
3	4.7	3.2	1.6	0.2	Iris-setosa
4	4.8	3.0	1.4	0.1	Iris-setosa
5	4.8	3.1	1.6	0.2	Iris-setosa
6	4.8	3.4	1.9	0.2	Iris-setosa
7	4.9	3.0	1.4	0.2	Iris-setosa
8	4.9	3.1	1.5	0.1	Iris-setosa
9	4.9	3.1	1.5	0.1	Iris-setosa
10	4.9	3.1	1.5	0.1	Iris-setosa
11	5.0	2.3	3.3	1.0	Iris-versicolor
12	5.0	3.4	1.5	0.2	Iris-setosa
13	5.1	3.5	1.4	0.2	Iris-setosa
14	5.4	3.0	4.5	1.5	Iris-versicolor
15	5.4	3.4	1.5	0.4	Iris-setosa
16	5.4	3.9	1.3	0.4	Iris-setosa
17	5.5	2.4	3.7	1.0	Iris-versicolor
18	5.5	2.4	3.8	1.1	Iris-versicolor
19	5.6	2.7	4.2	1.3	Iris-versicolor
20	5.7	3.0	4.2	1.2	Iris-versicolor
21	5.7	4.4	1.5	0.4	Iris-setosa
22	5.8	2.8	5.1	2.4	Iris-virginica
23	5.9	3.2	4.8	1.8	Iris-versicolor
24	6.1	3.0	4.6	1.4	Iris-versicolor
25	6.1	3.0	4.9	1.8	Iris-virginica
26	6.3	2.5	4.9	1.5	Iris-versicolor
27	6.3	3.3	4.7	1.6	Iris-versicolor
28	6.3	3.3	6.0	2.5	Iris-virginica
29	6.4	2.9	4.3	1.3	Iris-versicolor
30	6.5	3.0	5.5	1.8	Iris-virginica
31	6.5	3.0	5.8	2.2	Iris-virginica
32	6.7	3.0	5.0	1.7	Iris-versicolor
33	6.8	2.8	4.8	1.4	Iris-versicolor
34	6.8	3.2	5.9	2.3	Iris-virginica
35	7.0	3.2	4.7	1.4	Iris-versicolor
36	7.1	3.0	5.9	2.1	Iris-virginica
37	7.7	3.8	6.7	2.2	Iris-virginica
38	4.4	2.9	1.4	0.2	Iris-setosa
39	4.5	2.3	1.3	0.3	Iris-setosa
40	4.8	3.4	1.6	0.2	Iris-setosa
41	5.0	2.0	3.5	1.0	Iris-versicolor
42	5.1	3.3	1.7	0.5	Iris-setosa
43	5.1	3.4	1.5	0.2	Iris-setosa
44	5.2	2.7	3.9	1.4	Iris-versicolor
45	5.2	3.5	1.5	0.2	Iris-setosa
46	5.2	4.1	1.5	0.1	Iris-setosa
47	5.4	3.9	1.7	0.4	Iris-setosa
48	5.5	3.5	1.3	0.2	Iris-setosa
49	5.6	3.0	4.1	1.3	Iris-versicolor
50	5.8	2.7	3.9	1.2	Iris-versicolor
51	5.8	2.7	5.1	1.9	Iris-virginica
52	5.8	2.7	5.1	1.9	Iris-virginica
53	5.9	3.0	4.2	1.5	Iris-versicolor
54	5.9	3.0	5.1	1.8	Iris-virginica
55	6.0	2.7	5.1	1.6	Iris-versicolor
56	6.0	2.9	4.5	1.5	Iris-versicolor
57	6.1	2.8	4.7	1.2	Iris-versicolor
58	6.2	2.8	4.8	1.8	Iris-virginica
59	6.2	2.9	4.3	1.3	Iris-versicolor
60	6.3	2.3	4.4	1.3	Iris-versicolor
61	6.3	2.7	4.9	1.8	Iris-virginica
62	6.4	3.2	5.3	2.3	Iris-virginica
63	6.5	2.8	4.6	1.5	Iris-versicolor
64	6.5	3.0	5.2	2.0	Iris-virginica
65	6.5	3.2	5.1	2.0	Iris-virginica
66	6.6	2.9	4.6	1.3	Iris-versicolor
67	6.6	3.0	4.4	1.4	Iris-versicolor
68	6.7	3.1	4.4	1.4	Iris-versicolor
69	6.7	3.1	4.7	1.5	Iris-versicolor
70	6.9	3.1	4.9	1.5	Iris-versicolor
71	6.9	3.1	5.4	2.1	Iris-virginica
72	6.9	3.2	5.7	2.3	Iris-virginica
73	7.2	3.0	5.8	1.6	Iris-virginica
74	7.2	3.2	6.0	1.8	Iris-virginica
75	7.3	2.9	6.3	1.8	Iris-virginica
76	7.7	2.6	6.9	2.3	Iris-virginica
77	3.3	4.5	5.6	7.8	Iris-setosa
78	3.3	4.5	5.6	7.8	Iris-setosa
79	3.3	4.5	5.6	7.8	Iris-setosa
80	3.3	4.5	5.6	7.8	Iris-setosa
81	3.3	4.5	5.6	7.8	Iris-setosa
82	3.3	4.5	5.6	7.8	Iris-setosa
83	3.3	4.5	5.6	7.8	Iris-setosa
84	3.3	4.5	5.6	7.8	Iris-setosa
85	3.3	4.5	5.6	7.8	Iris-setosa
86	3.3	4.5	5.6	7.8	Iris-setosa
87	3.3	4.5	5.6	7.8	Iris-setosa
88	3.3	4.5	5.6	7.8	Iris-setosa
89	3.3	4.5	5.6	7.8	Iris-setosa
90	3.3	4.5	5.6	7.8	Iris-setosa
91	3.3	4.5	5.6	7.8	Iris-setosa
92	3.3	4.5	5.6	7.8	Iris-setosa
93	3.3	4.5	5.6	7.8	Iris-setosa
94	3.3	4.5	5.6	7.8	Iris-setosa
95	3.3	4.5	5.6	7.8	Iris-setosa
96	3.3	4.5	5.6	7.8	Iris-setosa
97	3.3	4.5	5.6	7.8	Iris-setosa
98	3.3	4.5	5.6	7.8	Iris-setosa
99	3.3	4.5	5.6	7.8	Iris-setosa
100	3.3	4.5	5.6	7.8	Iris-setosa

Rows: 1-100 | Columns: 5

Let’s import the model:

from verticapy.machine_learning.vertica import NearestCentroid

Then we can create the model:

model = NearestCentroid(p = 2)

We can now fit the model:

model.fit(
    train,
    [
        "SepalLengthCm",
        "SepalWidthCm",
        "PetalLengthCm",
        "PetalWidthCm",
    ],
    "Species",
    test,
)

We can get all the classification metrics using the classification_report:

model.classification_report()

	Iris-setosa	Iris-versicolor	Iris-virginica	avg_macro	avg_weighted	avg_micro
auc	1.0	0.9825000000000003	0.8799999999999999	0.9541666666666667	0.9484999999999999	[null]
prc_auc	1.0	0.9380196192696192	0.9033461507410405	0.9471219233368866	0.9489423841503399	[null]
accuracy	0.76	0.62	0.86	0.7466666666666666	0.7719999999999999	0.7466666666666667
log_loss	0.227702158471206	0.191498150629386	0.21611154240033	0.211770617166974	0.2158251104744916	[null]
precision	1.0	0.3448275862068966	1.0	0.7816091954022989	0.8689655172413794	0.62
recall	0.4	1.0	0.65	0.6833333333333332	0.62	0.62
f1_score	0.5714285714285715	0.5128205128205129	0.787878787878788	0.6240426240426241	0.6462870462870464	0.62
mcc	0.5345224838248488	0.4254814716983816	0.7259662712736914	0.561990075598974	0.5892917963790923	0.43
informedness	0.3999999999999999	0.5249999999999999	0.6499999999999999	0.5249999999999999	0.5249999999999999	0.43000000000000016
markedness	0.7142857142857144	0.3448275862068966	0.810810810810811	0.6233080371011407	0.6790041272799894	0.43000000000000016
csi	0.4	0.3448275862068966	0.65	0.4649425287356322	0.4889655172413794	0.4492753623188406

Rows: 1-11 | Columns: 7

Important

For this example, a specific model is utilized, and it may not correspond exactly to the model you are working with. To see a comprehensive example specific to your class of interest, please refer to that particular class.