vastorbit.VastFrame.score¶

VastFrame.score(y_true: str, y_score: str, metric: 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', 'r2', 'rsquared', 'mae', 'mean_absolute_error', 'mse', 'mean_squared_error', 'msle', 'mean_squared_log_error', 'max', 'max_error', 'median', 'median_absolute_error', 'var', 'explained_variance']) → float¶

Computes the score using the input columns and the input metric.

Parameters:

y_true (str) – Response column.
y_score (str) – Prediction.
metric (str) –
The metric used to compute the score.

For Classification
- accuracy:
  Accuracy.
  
  \[Accuracy = \frac{TP + TN}{TP + TN + FP + FN}\]
- 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.
- bm:
  Informedness.
  
  \[BA = TPR + TNR - 1\]
- 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.
  
  \[\frac{FP}{FP + TN}\]
- 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.
  
  \[DOR = \frac{TP \times TN}{FP \times FN}\]
- mcc:
  Matthews Correlation Coefficient.
  
  \[MCC = \frac{TP \times TN - FP \times FN}{\sqrt{(TP + FP)(TP + FN)(TN + FP)(TN + FN)}}\]
- 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.
  
  \[Specificity = \frac{TN}{TN + FP}\]
For Regression
- max:
  Max Error.
  
  \[ME = \max_{i=1}^{n} \left| y_i - \hat{y}_i \right|\]
- mae:
  Mean Absolute Error.
  
  \[MAE = \frac{1}{n} \sum_{i=1}^{n} \left| y_i - \hat{y}_i \right|\]
- median:
  Median Absolute Error.
  
  \[MedAE = \text{median}_{i=1}^{n} \left| y_i - \hat{y}_i \right|\]
- mse:
  Mean Squared Error.
  
  \[MSE = \frac{1}{n} \sum_{i=1}^{n} \left( y_i - \hat{y}_i \right)^2\]
- msle:
  Mean Squared Log Error.
  
  \[MSLE = \frac{1}{n} \sum_{i=1}^{n} (\log(1 + y_i) - \log(1 + \hat{y}_i))^2\]
- r2:
  R squared coefficient.
  
  \[R^2 = 1 - \frac{\sum_{i=1}^{n} (y_i - \hat{y}_i)^2}{\sum_{i=1}^{n} (y_i - \bar{y})^2}\]
- var:
  Explained Variance.
  
  \[\text{Explained Variance} = 1 - \frac{Var(y - \hat{y})}{Var(y)}\]

Returns:

score.

Return type:

float

Examples

Let us build a quick ML model and calculate the score of its predictions.

Load data for machine learning¶

For this example, we will use the winequality dataset.

import vastorbit.datasets as vod

data = vod.load_winequality()

	123 fixed_acidity Decimal(6, 3)	123 volatile_acidity Decimal(7, 4)	123 citric_acid Decimal(6, 3)	123 residual_sugar Decimal(7, 3)	123 chlorides Double	123 free_sulfur_dioxide Decimal(7, 2)	123 total_sulfur_dioxide Decimal(7, 2)	123 density Double	123 ph Decimal(6, 3)	123 sulphates Decimal(6, 3)	123 alcohol Double	123 quality Integer	123 good Integer	Abc color Varchar(20)
1	6.3	0.67	0.48	12.6	0.052	57.0	222.0	0.9979	3.17	0.52	9.3	6	0	white
2	7.4	0.4	0.29	5.4	0.044	31.0	122.0	0.994	3.3	0.5	11.1	8	1	white
3	7.1	0.26	0.31	2.2	0.044	29.0	128.0	0.9937	3.34	0.64	10.9	8	1	white
4	9.0	0.31	0.48	6.6	0.043	11.0	73.0	0.9938	2.9	0.38	11.6	5	0	white
5	6.3	0.39	0.24	6.9	0.069	9.0	117.0	0.9942	3.15	0.35	10.2	4	0	white
6	8.2	0.22	0.36	6.8	0.034	12.0	90.0	0.9944	3.01	0.38	10.5	8	1	white
7	7.1	0.19	0.28	3.6	0.033	16.0	78.0	0.993	2.91	0.78	11.4	6	0	white
8	7.3	0.25	0.36	13.1	0.05	35.0	200.0	0.9986	3.04	0.46	8.9	7	1	white
9	7.9	0.2	0.34	1.2	0.04	29.0	118.0	0.9932	3.14	0.41	10.6	6	0	white
10	7.1	0.26	0.32	5.9	0.037	39.0	97.0	0.9934	3.31	0.4	11.6	6	0	white
11	7.0	0.2	0.34	5.7	0.035	32.0	83.0	0.9928	3.19	0.46	11.5	6	0	white
12	6.9	0.3	0.33	4.1	0.035	26.0	155.0	0.9925	3.25	0.79	12.3	8	1	white
13	8.1	0.29	0.49	7.1	0.042	22.0	124.0	0.9944	3.14	0.41	10.8	6	0	white
14	5.8	0.17	0.3	1.4	0.037	55.0	130.0	0.9909	3.29	0.38	11.3	6	0	white
15	5.9	0.415	0.02	0.8	0.038	22.0	63.0	0.9932	3.36	0.36	9.3	5	0	white
16	6.6	0.23	0.26	1.3	0.045	16.0	128.0	0.9934	3.36	0.6	10.0	6	0	white
17	8.6	0.55	0.35	15.55	0.057	35.5	366.5	1.0001	3.04	0.63	11.0	3	0	white
18	6.9	0.35	0.74	1.0	0.044	18.0	132.0	0.992	3.13	0.55	10.2	5	0	white
19	7.6	0.14	0.74	1.6	0.04	27.0	103.0	0.9916	3.07	0.4	10.8	7	1	white
20	9.2	0.28	0.49	11.8	0.042	29.0	137.0	0.998	3.1	0.34	10.1	4	0	white
21	6.2	0.18	0.49	4.5	0.047	17.0	90.0	0.9919	3.27	0.37	11.6	6	0	white
22	5.3	0.165	0.24	1.1	0.051	25.0	105.0	0.9925	3.32	0.47	9.1	5	0	white
23	9.8	0.25	0.74	10.0	0.056	36.0	225.0	0.9977	3.06	0.43	10.0	4	0	white
24	8.1	0.29	0.49	7.1	0.042	22.0	124.0	0.9944	3.14	0.41	10.8	6	0	white
25	6.8	0.22	0.49	0.9	0.052	26.0	128.0	0.991	3.25	0.35	11.4	6	0	white
26	7.2	0.22	0.49	1.0	0.045	34.0	140.0	0.99	3.05	0.34	12.7	6	0	white
27	7.4	0.25	0.49	1.1	0.042	35.0	156.0	0.9917	3.13	0.55	11.3	5	0	white
28	8.2	0.18	0.49	1.1	0.033	28.0	81.0	0.9923	3.0	0.68	10.4	7	1	white
29	6.1	0.22	0.49	1.5	0.051	18.0	87.0	0.9928	3.3	0.46	9.6	5	0	white
30	7.0	0.39	0.24	1.0	0.048	8.0	119.0	0.9923	3.0	0.31	10.1	4	0	white
31	6.1	0.22	0.49	1.5	0.051	18.0	87.0	0.9928	3.3	0.46	9.6	5	0	white
32	6.5	0.36	0.49	2.9	0.03	16.0	94.0	0.9902	3.1	0.49	12.1	7	1	white
33	7.1	0.29	0.49	1.2	0.031	32.0	99.0	0.9893	3.07	0.33	12.2	6	0	white
34	7.4	0.25	0.49	1.1	0.042	35.0	156.0	0.9917	3.13	0.55	11.3	5	0	white
35	6.9	0.23	0.24	14.2	0.053	19.0	94.0	0.9982	3.17	0.5	9.6	5	0	white
36	8.5	0.56	0.74	17.85	0.051	51.0	243.0	1.0005	2.99	0.7	9.2	5	0	white
37	8.2	0.18	0.49	1.1	0.033	28.0	81.0	0.9923	3.0	0.68	10.4	7	1	white
38	6.3	0.23	0.49	7.1	0.05	67.0	210.0	0.9951	3.23	0.34	9.5	5	0	white
39	6.1	0.25	0.49	7.6	0.052	67.0	226.0	0.9956	3.16	0.47	8.9	5	0	white
40	7.2	0.26	0.74	13.6	0.05	56.0	162.0	0.998	3.03	0.44	8.8	5	0	white
41	7.2	0.31	0.24	1.4	0.057	17.0	117.0	0.9928	3.16	0.35	10.5	5	0	white
42	8.0	0.25	0.49	1.2	0.061	27.0	117.0	0.9938	3.08	0.34	9.4	5	0	white
43	7.0	0.18	0.49	5.3	0.04	34.0	125.0	0.9914	3.24	0.4	12.2	6	0	white
44	7.8	0.43	0.49	13.0	0.033	37.0	158.0	0.9955	3.14	0.35	11.3	6	0	white
45	8.3	0.2	0.74	4.45	0.044	33.0	130.0	0.9924	3.25	0.42	12.2	6	0	white
46	6.3	0.27	0.49	1.2	0.063	35.0	92.0	0.9911	3.38	0.42	12.2	6	0	white
47	7.4	0.16	0.49	1.2	0.055	18.0	150.0	0.9917	3.23	0.47	11.2	6	0	white
48	7.4	0.16	0.49	1.2	0.055	18.0	150.0	0.9917	3.23	0.47	11.2	6	0	white
49	6.9	0.19	0.49	6.6	0.036	49.0	172.0	0.9932	3.2	0.27	11.5	6	0	white
50	7.8	0.43	0.49	13.0	0.033	37.0	158.0	0.9955	3.14	0.35	11.3	6	0	white
51	7.2	0.4	0.49	1.1	0.048	11.0	138.0	0.9929	3.01	0.42	9.3	5	0	white
52	7.8	0.43	0.49	13.0	0.033	37.0	158.0	0.9955	3.14	0.35	11.3	6	0	white
53	7.6	0.52	0.49	14.0	0.034	37.0	156.0	0.9958	3.14	0.38	11.8	7	1	white
54	8.3	0.21	0.49	19.8	0.054	50.0	231.0	1.0012	2.99	0.54	9.2	5	0	white
55	6.9	0.34	0.74	11.2	0.069	44.0	150.0	0.9968	3.0	0.81	9.2	5	0	white
56	6.3	0.27	0.49	1.2	0.063	35.0	92.0	0.9911	3.38	0.42	12.2	6	0	white
57	8.3	0.2	0.74	4.45	0.044	33.0	130.0	0.9924	3.25	0.42	12.2	6	0	white
58	7.1	0.22	0.74	2.7	0.044	42.0	144.0	0.991	3.31	0.41	12.2	6	0	white
59	7.9	0.11	0.49	4.5	0.048	27.0	133.0	0.9946	3.24	0.42	10.6	6	0	white
60	8.5	0.17	0.74	3.6	0.05	29.0	128.0	0.9928	3.28	0.4	12.4	6	0	white
61	6.4	0.145	0.49	5.4	0.048	54.0	164.0	0.9946	3.56	0.44	10.8	6	0	white
62	7.4	0.16	0.49	1.2	0.055	18.0	150.0	0.9917	3.23	0.47	11.2	6	0	white
63	8.3	0.19	0.49	1.2	0.051	11.0	137.0	0.9918	3.06	0.46	11.0	6	0	white
64	8.0	0.44	0.49	9.1	0.031	46.0	151.0	0.9926	3.16	0.27	12.7	8	1	white
65	7.0	0.2	0.74	0.8	0.044	19.0	163.0	0.9931	3.46	0.53	10.2	5	0	white
66	6.9	0.19	0.49	6.6	0.036	49.0	172.0	0.9932	3.2	0.27	11.5	6	0	white
67	7.1	0.25	0.49	3.0	0.03	30.0	96.0	0.9903	3.13	0.39	12.3	7	1	white
68	6.5	0.24	0.24	1.6	0.046	15.0	60.0	0.9928	3.19	0.39	9.8	5	0	white
69	7.2	0.4	0.49	1.1	0.048	11.0	138.0	0.9929	3.01	0.42	9.3	5	0	white
70	7.6	0.52	0.49	14.0	0.034	37.0	156.0	0.9958	3.14	0.38	11.8	7	1	white
71	7.8	0.43	0.49	13.0	0.033	37.0	158.0	0.9955	3.14	0.35	11.3	6	0	white
72	7.8	0.21	0.49	1.35	0.052	6.0	48.0	0.9911	3.15	0.28	11.4	5	0	white
73	7.0	0.2	0.49	5.9	0.038	39.0	128.0	0.9938	3.21	0.48	10.8	6	0	white
74	6.9	0.25	0.24	3.6	0.057	13.0	85.0	0.9942	2.99	0.48	9.5	4	0	white
75	7.2	0.08	0.49	1.3	0.05	18.0	148.0	0.9945	3.46	0.44	10.2	6	0	white
76	7.1	0.85	0.49	8.7	0.028	40.0	184.0	0.9962	3.22	0.36	10.7	5	0	white
77	7.6	0.51	0.24	1.2	0.04	10.0	104.0	0.992	3.05	0.29	10.8	6	0	white
78	7.9	0.22	0.24	4.6	0.044	39.0	159.0	0.9927	2.99	0.28	11.5	6	0	white
79	7.7	0.16	0.49	2.0	0.056	20.0	124.0	0.9948	3.32	0.49	10.7	6	0	white
80	7.2	0.08	0.49	1.3	0.05	18.0	148.0	0.9945	3.46	0.44	10.2	6	0	white
81	6.6	0.25	0.24	1.7	0.048	26.0	124.0	0.9942	3.37	0.6	10.1	6	0	white
82	6.7	0.16	0.49	2.4	0.046	57.0	187.0	0.9952	3.62	0.81	10.4	6	0	white
83	6.9	0.25	0.24	3.6	0.057	13.0	85.0	0.9942	2.99	0.48	9.5	4	0	white
84	7.5	0.32	0.24	4.6	0.053	8.0	134.0	0.9958	3.14	0.5	9.1	3	0	white
85	7.4	0.28	0.49	1.5	0.034	20.0	126.0	0.9918	2.98	0.39	10.6	6	0	white
86	6.2	0.15	0.49	0.9	0.033	17.0	51.0	0.9932	3.3	0.7	9.4	6	0	white
87	6.7	0.25	0.74	19.4	0.054	44.0	169.0	1.0004	3.51	0.45	9.8	6	0	white
88	6.5	0.26	0.74	13.3	0.044	68.0	224.0	0.9972	3.18	0.54	9.5	6	0	white
89	7.9	0.16	0.74	17.85	0.037	52.0	187.0	0.9998	2.99	0.41	9.3	5	0	white
90	5.6	0.185	0.49	1.1	0.03	28.0	117.0	0.9918	3.55	0.45	10.3	6	0	white
91	7.5	0.2	0.49	1.3	0.031	8.0	97.0	0.9918	3.06	0.62	11.1	5	0	white
92	8.0	0.3	0.49	9.4	0.046	47.0	188.0	0.9964	3.14	0.48	10.0	5	0	white
93	8.0	0.34	0.49	9.0	0.033	39.0	180.0	0.9936	3.13	0.38	12.3	8	1	white
94	7.7	0.35	0.49	8.65	0.033	42.0	186.0	0.9931	3.14	0.38	12.4	8	1	white
95	7.6	0.29	0.49	9.6	0.03	45.0	197.0	0.9938	3.13	0.38	12.3	7	1	white
96	6.7	0.62	0.24	1.1	0.039	6.0	62.0	0.9934	3.41	0.32	10.4	5	0	white
97	6.8	0.27	0.49	1.2	0.044	35.0	126.0	0.99	3.13	0.48	12.1	7	1	white
98	7.7	0.27	0.49	1.8	0.041	23.0	86.0	0.9914	3.16	0.42	12.5	6	0	white
99	6.7	0.51	0.24	2.1	0.043	14.0	155.0	0.9904	3.22	0.6	13.0	6	0	white
100	7.4	0.19	0.49	9.3	0.03	26.0	132.0	0.994	2.99	0.32	11.0	7	1	white

Rows: 1-100 | Columns: 14

Note

vastorbit offers a wide range of sample datasets that are ideal for training and testing purposes. You can explore the full list of available datasets in the Datasets, which provides detailed information on each dataset and how to use them effectively. These datasets are invaluable resources for honing your data analysis and machine learning skills within the vastorbit environment.

You can easily divide your dataset into training and testing subsets using the VastFrame.train_test_split() method.

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

Model Initialization¶

First we import the LinearRegression model:

from vastorbit.machine_learning.vast import LinearRegression

Then we can create the model:

model = LinearRegression(
    tol = 1e-6,
    fit_intercept = True,
)

Model Training¶

We can now fit the model:

model.fit(
    train,
    [
        "fixed_acidity",
        "volatile_acidity",
        "citric_acid",
        "residual_sugar",
        "chlorides",
        "density"
    ],
    "quality",
    test,
)

Prediction¶

Prediction is straight-forward:

result = model.predict(
    test,
    [
        "fixed_acidity",
        "volatile_acidity",
        "citric_acid",
        "residual_sugar",
        "chlorides",
        "density"
    ],
    "prediction",
)

	123 fixed_acidity Decimal(6, 3)	123 volatile_acidity Decimal(7, 4)	123 citric_acid Decimal(6, 3)	123 residual_sugar Decimal(7, 3)	123 chlorides Double	123 free_sulfur_dioxide Decimal(7, 2)	123 total_sulfur_dioxide Decimal(7, 2)	123 density Double	123 ph Decimal(6, 3)	123 sulphates Decimal(6, 3)	123 alcohol Double	123 quality Integer	123 good Integer	Abc color Varchar(20)	123 seedrand Decimal(26, 6)	123 prediction Double
1	7.4	0.7	0.0	1.9	0.076	11.0	34.0	0.9978	3.51	0.56	9.4	5	0	red	0.12	5.0160030375046745
2	7.8	0.76	0.04	2.3	0.092	15.0	54.0	0.997	3.26	0.65	9.8	5	0	red	0.11	5.154165628980678
3	7.4	0.66	0.0	1.8	0.075	13.0	40.0	0.9978	3.51	0.56	9.4	5	0	red	0.11	5.042160469606813
4	7.5	0.5	0.36	6.1	0.071	17.0	102.0	0.9978	3.35	0.8	10.5	5	0	red	0.18	5.321140644907786
5	8.5	0.49	0.11	2.3	0.084	9.0	67.0	0.9968	3.17	0.53	9.4	5	0	red	0.1	5.482509891283485
6	7.8	0.645	0.0	2.0	0.0819999999999999	8.0	16.0	0.9964	3.38	0.59	9.8	6	0	red	0.13	5.3193499127730774
7	6.9	0.605	0.12	10.7	0.073	40.0	83.0	0.9993	3.45	0.52	9.4	6	0	red	0.11	5.169084603365491
8	8.7	0.29	0.52	1.6	0.113	12.0	37.0	0.9969	3.25	0.58	9.5	5	0	red	0.11	5.567550140224029
9	6.4	0.4	0.23	1.6	0.066	5.0	12.0	0.9958	3.34	0.56	9.2	5	0	red	0.06	5.341099158553561
10	5.6	0.31	0.37	1.4	0.074	12.0	96.0	0.9954	3.32	0.58	9.2	5	0	red	0.07	5.322832083240257
11	7.5	0.63	0.12	5.1	0.111	50.0	110.0	0.9983	3.26	0.77	9.4	5	0	red	0.14	5.135049695149519
12	7.3	0.39	0.31	2.4	0.074	9.0	46.0	0.9962	3.41	0.54	9.4	6	0	red	0.07	5.448270388433826
13	7.5	0.52	0.16	1.9	0.085	12.0	35.0	0.9968	3.38	0.62	9.5	7	1	red	0.09	5.289546490791139
14	7.2	0.725	0.05	4.65	0.086	4.0	11.0	0.9962	3.41	0.39	10.9	5	0	red	0.04	5.307507777184554
15	9.3	0.32	0.57	2.0	0.074	27.0	65.0	0.9969	3.28	0.79	10.7	5	0	red	0.1	5.646053056178914
16	8.0	0.705	0.05	1.9	0.074	8.0	19.0	0.9962	3.34	0.95	10.5	6	0	red	0.08	5.321387905926201
17	7.7	0.69	0.22	1.9	0.084	18.0	94.0	0.9961	3.31	0.48	9.5	5	0	red	0.03	5.281616475657728
18	9.7	0.32	0.54	2.5	0.094	28.0	83.0	0.9984	3.28	0.82	9.6	5	0	red	0.17	5.515896944285089
19	8.3	0.625	0.2	1.5	0.08	27.0	119.0	0.9972	3.16	1.12	9.1	4	0	red	0.0	5.2481234461306485
20	6.2	0.45	0.2	1.6	0.069	3.0	15.0	0.9958	3.41	0.56	9.2	5	0	red	0.12	5.277245528785613
21	7.3	0.67	0.26	1.8	0.401	16.0	51.0	0.9969	3.16	1.14	9.4	5	0	red	0.04	5.097760906235635
22	8.6	0.49	0.28	1.9	0.11	20.0	136.0	0.9972	2.93	1.95	9.9	6	0	red	0.08	5.4007071371740665
23	7.0	0.62	0.08	1.8	0.076	8.0	24.0	0.9978	3.48	0.53	9.0	5	0	red	0.09	5.0040916109269915
24	8.6	0.49	0.28	1.9	0.11	20.0	136.0	0.9972	2.93	1.95	9.9	6	0	red	0.09	5.4007071371740665
25	4.7	0.6	0.17	2.3	0.0579999999999999	17.0	106.0	0.9932	3.85	0.6	12.9	6	0	red	0.17	5.347151597133632
26	8.0	0.33	0.53	2.5	0.091	18.0	80.0	0.9976	3.37	0.8	9.6	6	0	red	0.14	5.37409888945902
27	10.1	0.31	0.44	2.3	0.08	22.0	46.0	0.9988	3.32	0.67	9.7	6	0	red	0.14	5.529700086968404
28	7.8	0.56	0.12	2.0	0.0819999999999999	7.0	28.0	0.997	3.37	0.5	9.4	6	0	red	0.14	5.284089412034518
29	6.1	0.38	0.15	1.8	0.072	6.0	19.0	0.9955	3.42	0.57	9.4	5	0	red	0.14	5.373109138619384
30	7.2	0.415	0.36	2.0	0.081	13.0	45.0	0.9972	3.48	0.64	9.2	5	0	red	0.0	5.248678421520566
31	7.6	0.49	0.26	1.6	0.236	10.0	88.0	0.9968	3.11	0.8	9.3	5	0	red	0.09	5.2933875751626545
32	7.3	0.33	0.47	2.1	0.077	5.0	11.0	0.9958	3.33	0.53	10.3	6	0	red	0.12	5.517934714577137
33	7.1	0.43	0.42	5.5	0.07	29.0	129.0	0.9973	3.42	0.72	10.5	5	0	red	0.18	5.3534592107420735
34	6.8	0.6	0.18	1.9	0.079	18.0	86.0	0.9968	3.59	0.57	9.3	6	0	red	0.13	5.123930333905605
35	7.6	0.95	0.03	2.0	0.09	7.0	20.0	0.9959	3.2	0.56	9.6	5	0	red	0.16	5.1248240023767835
36	7.3	0.59	0.26	7.2	0.07	35.0	121.0	0.9981	3.37	0.49	9.4	5	0	red	0.04	5.240758743954018
37	7.2	0.73	0.02	2.5	0.076	16.0	42.0	0.9972	3.44	0.52	9.3	5	0	red	0.11	5.072646889188235
38	7.9	0.5	0.33	2.0	0.084	15.0	143.0	0.9968	3.2	0.55	9.5	5	0	red	0.19	5.346939180082984
39	7.8	0.59	0.33	2.0	0.074	24.0	120.0	0.9968	3.25	0.54	9.4	5	0	red	0.06	5.264345038505667
40	7.0	0.43	0.36	1.6	0.089	14.0	37.0	0.99615	3.34	0.56	9.2	6	0	red	0.11	5.339289262376383
41	7.8	0.44	0.28	2.7	0.1	18.0	95.0	0.9966	3.22	0.67	9.4	5	0	red	0.18	5.441956284457149
42	11.0	0.3	0.58	2.1	0.054	7.0	19.0	0.998	3.31	0.88	10.5	7	1	red	0.01	5.758462074287053
43	11.6	0.44	0.64	2.1	0.059	5.0	15.0	0.998	3.21	0.67	10.2	6	0	red	0.07	5.732241847565831
44	7.0	0.49	0.49	5.6	0.06	26.0	121.0	0.9974	3.34	0.76	10.5	5	0	red	0.06	5.275240156780228
45	6.8	0.61	0.04	1.5	0.057	5.0	10.0	0.99525	3.42	0.6	9.5	5	0	red	0.1	5.337521758380177
46	7.7	0.43	0.25	2.6	0.073	29.0	63.0	0.99615	3.37	0.58	10.5	6	0	red	0.15	5.499662096103606
47	8.2	1.0	0.09	2.3	0.065	7.0	37.0	0.99685	3.32	0.55	9.0	6	0	red	0.08	5.046833210997164
48	7.2	0.63	0.0	1.9	0.0969999999999999	14.0	38.0	0.99675	3.37	0.58	9.0	6	0	red	0.1	5.187927239087713
49	15.0	0.21	0.44	2.2	0.075	10.0	24.0	1.00005	3.07	0.84	9.2	7	1	red	0.16	6.142504562720916
50	9.0	0.31	0.48	6.6	0.043	11.0	73.0	0.9938	2.9	0.38	11.6	5	0	white	0.07	6.262277673240931
51	6.3	0.39	0.24	6.9	0.069	9.0	117.0	0.9942	3.15	0.35	10.2	4	0	white	0.05	5.789798179270804
52	8.2	0.22	0.36	6.8	0.034	12.0	90.0	0.9944	3.01	0.38	10.5	8	1	white	0.11	6.152166326034205
53	5.8	0.17	0.3	1.4	0.037	55.0	130.0	0.9909	3.29	0.38	11.3	6	0	white	0.16	6.108488144047726
54	5.3	0.165	0.24	1.1	0.051	25.0	105.0	0.9925	3.32	0.47	9.1	5	0	white	0.11	5.805312374789622
55	6.1	0.22	0.49	1.5	0.051	18.0	87.0	0.9928	3.3	0.46	9.6	5	0	white	0.19	5.824799592249121
56	6.1	0.25	0.49	7.6	0.052	67.0	226.0	0.9956	3.16	0.47	8.9	5	0	white	0.07	5.668798745054744
57	7.2	0.31	0.24	1.4	0.057	17.0	117.0	0.9928	3.16	0.35	10.5	5	0	white	0.01	5.943567170561323
58	8.3	0.2	0.74	4.45	0.044	33.0	130.0	0.9924	3.25	0.42	12.2	6	0	white	0.07	6.31701016384082
59	7.6	0.52	0.49	14.0	0.034	37.0	156.0	0.9958	3.14	0.38	11.8	7	1	white	0.03	5.933410044071763
60	6.9	0.34	0.74	11.2	0.069	44.0	150.0	0.9968	3.0	0.81	9.2	5	0	white	0.04	5.671675605966783
61	8.3	0.2	0.74	4.45	0.044	33.0	130.0	0.9924	3.25	0.42	12.2	6	0	white	0.19	6.31701016384082
62	6.4	0.145	0.49	5.4	0.048	54.0	164.0	0.9946	3.56	0.44	10.8	6	0	white	0.01	5.839514114120476
63	7.8	0.43	0.49	13.0	0.033	37.0	158.0	0.9955	3.14	0.35	11.3	6	0	white	0.07	6.030509331287192
64	7.2	0.08	0.49	1.3	0.05	18.0	148.0	0.9945	3.46	0.44	10.2	6	0	white	0.07	5.842511014648437
65	6.7	0.16	0.49	2.4	0.046	57.0	187.0	0.9952	3.62	0.81	10.4	6	0	white	0.14	5.6568435794747245
66	7.4	0.28	0.49	1.5	0.034	20.0	126.0	0.9918	2.98	0.39	10.6	6	0	white	0.09	6.11269027188132
67	7.7	0.27	0.49	1.8	0.041	23.0	86.0	0.9914	3.16	0.42	12.5	6	0	white	0.13	6.2337171194849645
68	8.1	0.2	0.49	8.1	0.051	51.0	205.0	0.9954	3.1	0.52	11.0	6	0	white	0.08	6.050185950292473
69	7.8	0.26	0.74	7.5	0.044	59.0	160.0	0.996	3.22	0.64	10.0	6	0	white	0.14	5.819017472263283
70	8.3	0.2	0.49	1.7	0.038	38.0	167.0	0.9939	3.05	0.37	10.1	6	0	white	0.07	6.015682142899465
71	6.6	0.3	0.24	1.2	0.034	17.0	121.0	0.9933	3.13	0.36	9.2	5	0	white	0.02	5.784802230256162
72	8.0	0.25	0.49	9.0	0.044	31.0	185.0	0.998	3.34	0.49	10.0	6	0	white	0.01	5.667609235351335
73	6.6	0.21	0.49	18.15	0.042	41.0	158.0	0.9997	3.28	0.39	8.7	6	0	white	0.02	5.648343348656454
74	7.9	0.28	0.49	7.7	0.045	48.0	195.0	0.9954	3.04	0.55	11.0	6	0	white	0.14	5.942970864378907
75	7.4	0.27	0.49	1.1	0.037	33.0	156.0	0.992	3.15	0.54	11.1	6	0	white	0.15	6.074376999092294
76	7.1	0.18	0.74	15.6	0.044	44.0	176.0	0.9996	3.38	0.67	9.0	6	0	white	0.02	5.6172850726999
77	6.0	0.17	0.49	1.0	0.034	26.0	106.0	0.992	3.21	0.42	9.8	6	0	white	0.07	5.941072781921491
78	7.2	0.16	0.49	1.3	0.037	27.0	104.0	0.9924	3.23	0.57	10.6	6	0	white	0.04	6.0806273399980455
79	7.3	0.14	0.49	1.1	0.038	28.0	99.0	0.9928	3.2	0.72	10.6	6	0	white	0.16	6.044961902634725
80	7.9	0.12	0.49	5.2	0.049	33.0	152.0	0.9952	3.18	0.47	10.6	6	0	white	0.01	5.984478138136694
81	8.5	0.15	0.49	1.5	0.031	17.0	122.0	0.9932	3.03	0.4	10.3	6	0	white	0.19	6.174188240804256
82	7.3	0.21	0.49	1.8	0.038	44.0	152.0	0.9912	3.32	0.44	12.6	7	1	white	0.05	6.249312066144881
83	7.9	0.18	0.49	5.2	0.051	36.0	157.0	0.9953	3.18	0.48	10.6	6	0	white	0.02	5.9245045492462225
84	7.8	0.3	0.74	1.8	0.033	33.0	156.0	0.991	3.29	0.52	12.8	6	0	white	0.13	6.252131640149116
85	7.8	0.3	0.74	1.8	0.033	33.0	156.0	0.991	3.29	0.52	12.8	6	0	white	0.03	6.252131640149116
86	6.7	0.3	0.74	5.0	0.038	35.0	157.0	0.9945	3.21	0.46	9.9	5	0	white	0.02	5.732274426181135
87	6.6	0.3	0.74	4.6	0.041	36.0	159.0	0.9946	3.21	0.45	9.9	5	0	white	0.1	5.685895451259711
88	6.8	0.17	0.74	2.4	0.053	61.0	182.0	0.9953	3.63	0.76	10.5	6	0	white	0.14	5.618628878766344
89	7.0	0.27	0.74	1.5	0.036	27.0	122.0	0.9926	3.35	0.48	11.2	6	0	white	0.13	5.917250325736319
90	6.9	0.27	0.49	23.5	0.057	59.0	235.0	1.0024	2.98	0.47	8.6	5	0	white	0.03	5.494388150204742
91	7.5	0.32	0.49	1.7	0.031	44.0	109.0	0.9906	3.07	0.46	12.5	6	0	white	0.02	6.2761941594745565
92	7.8	0.26	0.49	3.2	0.027	28.0	87.0	0.9919	3.03	0.32	11.3	7	1	white	0.06	6.246468465176122
93	8.9	0.32	0.49	1.6	0.05	17.0	131.0	0.9956	3.13	0.34	9.4	5	0	white	0.07	5.765857140803007
94	7.0	0.14	0.49	5.9	0.053	22.0	118.0	0.9954	3.36	0.36	9.4	6	0	white	0.11	5.839057104010095
95	7.8	0.15	0.24	7.7	0.047	21.0	98.0	0.9951	2.94	0.31	9.6	6	0	white	0.12	6.100412451506088
96	6.9	0.25	0.24	1.8	0.053	6.0	121.0	0.993	3.23	0.7	11.4	5	0	white	0.09	5.934412427243586
97	8.1	0.12	0.49	1.2	0.042	43.0	160.0	0.9934	3.13	0.48	9.7	6	0	white	0.1	6.096351271957161
98	7.3	0.19	0.49	15.55	0.058	50.0	134.0	0.9998	3.42	0.36	9.1	7	1	white	0.01	5.638315272647333
99	7.5	0.24	0.49	9.4	0.048	50.0	149.0	0.9962	3.17	0.59	10.5	7	1	white	0.11	5.874714492249041
100	6.9	0.26	0.49	1.6	0.058	39.0	166.0	0.9965	3.65	0.52	9.4	4	0	white	0.1	5.390052860592874

Rows: 1-100 | Columns: 16

Score¶

Finally we can calculate the scores:

# R2
result.score("quality", "prediction", metric = "r2")
#  MSE
result.score("quality", "prediction", metric = "mse")
#  Max Error
result.score("quality", "prediction", metric = "max")

Note

If the prediction is already part of the dataset, there is no need to use a model to compute a prediction column. Use your column directly.