randomized_features_search_cv¶

In [ ]:

randomized_features_search_cv(estimator,
                              input_relation: (str, vDataFrame),
                              X: list,
                              y: str,
                              metric: str = "auto",
                              cv: int = 3,
                              pos_label: (int, float, str) = None,
                              cutoff: float = -1,
                              training_score: bool = True,
                              comb_limit: int = 100,
                              skip_error: bool = True,
                              print_info: bool = True,)

Computes the k-fold grid search of an estimator using different combinations of features. It can be used to find parameters that optimize the model.

Parameters¶

Name	Type	Optional	Description
estimator	object	❌	Vertica estimator having a fit method.
input_relation	str / vDataFrame	❌	Relation to use to train the model.
X	list	❌	List of the predictor columns.
y	str	❌	Response Column.
metric	str / list	✓	Metric used to do the model evaluation. auto : logloss for classification & rmse for regression. For Classification: accuracy : Accuracy auc : Area Under the Curve (ROC) bm : Informedness = tpr + tnr - 1 csi : Critical Success Index = tp / (tp + fn + fp) f1 : F1 Score logloss : Log Loss mcc : Matthews Correlation Coefficient mk : Markedness = ppv + npv - 1 npv : Negative Predictive Value = tn / (tn + fn) prc_auc : Area Under the Curve (PRC) precision : Precision = tp / (tp + fp) recall : Recall = tp / (tp + fn) specificity : Specificity = tn / (tn + fp) For Regression: max : Max Error mae : Mean Absolute Error median : Median Absolute Error mse : Mean Squared Error msle : Mean Squared Log Error r2 : R-squared coefficient r2a : R2 adjusted rmse : Root Mean Squared Error var : Explained Variance
cv	int	✓	Number of folds.
pos_label	int / float / str	✓	The main class to be considered as positive (classification only).
cutoff	float	✓	The model cutoff (classification only).
training_score	bool	✓	If set to True, the training score will be computed with the validation score.
comb_limit	int	✓	Maximum number of features combinations used to train the model.
skip_error	bool	✓	If set to True and an error occurs, it will be displayed and not raised.
print_info	bool	✓	If set to True, prints the model information at each step.

Returns¶

tablesample : An object containing the result. For more information, see utilities.tablesample.

Example¶

In [61]:

from verticapy.learn.linear_model import LogisticRegression
model = LogisticRegression(name = "public.LR_titanic",
                           tol = 1e-4,
                           max_iter = 100, 
                           solver = 'Newton')

from verticapy.learn.model_selection import randomized_features_search_cv
randomized_features_search_cv(model,
                              input_relation = "public.titanic", 
                              X = ["age", "fare", "pclass"], 
                              y = "survived", 
                              cv = 3)

Model: LogisticRegression; Features: ['age']; Test_score: 0.29739591302201435; Train_score: 0.2870846295987417; Time: 0.23079792658487955;
Model: LogisticRegression; Features: ['fare']; Test_score: 0.2622687023390637; Train_score: 0.27165475232364034; Time: 0.25974663098653156;
Model: LogisticRegression; Features: ['pclass']; Test_score: 0.26401495708324; Train_score: 0.25868874967928734; Time: 0.22532439231872559;
Model: LogisticRegression; Features: ['age', 'fare']; Test_score: 0.27261136467610336; Train_score: 0.271655326046594; Time: 0.22174525260925293;
Model: LogisticRegression; Features: ['age', 'pclass']; Test_score: 0.26075270983439364; Train_score: 0.2546633029212083; Time: 0.21323863665262857;
Model: LogisticRegression; Features: ['fare', 'pclass']; Test_score: 0.25272124780890964; Train_score: 0.262128818022629; Time: 0.21834190686543783;
Model: LogisticRegression; Features: ['age', 'fare', 'pclass']; Test_score: 0.2663715923369417; Train_score: 0.25077000202031735; Time: 0.2347706158955892;

Randomized Features Search Selected Model
LogisticRegression; Features: ['fare', 'pclass']; Test_score: 0.25272124780890964; Train_score: 0.262128818022629; Time: 0.21834190686543783;

Out[61]:

	features	avg_score	avg_train_score	avg_time	score_std	score_train_std
1	['fare', 'pclass']	0.25272124780890964	0.262128818022629	0.21834190686543783	7.043012041026667e-05	0.00017019024915138368
2	['age', 'pclass']	0.26075270983439364	0.2546633029212083	0.21323863665262857	0.002076905164550426	0.0006827360831617877
3	['fare']	0.2622687023390637	0.27165475232364034	0.25974663098653156	0.0037831907867619933	0.0020373834096355167
4	['pclass']	0.26401495708324	0.25868874967928734	0.22532439231872559	0.004087209772525585	0.0022203907434587234
5	['age', 'fare', 'pclass']	0.2663715923369417	0.25077000202031735	0.2347706158955892	0.008440420431771319	0.004107429392604143
6	['age', 'fare']	0.27261136467610336	0.271655326046594	0.22174525260925293	0.006434243291596136	0.0030117916923140657
7	['age']	0.29739591302201435	0.2870846295987417	0.23079792658487955	0.0033343980447430397	0.0017523334554747426

Rows: 1-7 | Columns: 6