Model.fit¶

In [ ]:

Model.fit(input_relation: (str, vDataFrame),
          X: list,
          y: str,)

Trains the model.

Parameters¶

Name	Type	Optional	Description
input_relation	str / vDataFrame	❌	Training relation.
X	list	❌	List of the predictors.
y	str	❌	Response column.

Returns¶

object : model grid

Example¶

In [1]:

from verticapy.learn.delphi import AutoML

model = AutoML("titanic_autoML", stepwise = False)
model.fit("public.titanic", y = "survived")

Starting AutoML

Testing Model - LogisticRegression

Model: LogisticRegression; Parameters: {'tol': 1e-06, 'max_iter': 100, 'penalty': 'none', 'solver': 'bfgs'}; Test_score: 0.058747133750518495; Train_score: 0.030520459056835366; Time: 10.177567720413208;
Model: LogisticRegression; Parameters: {'tol': 1e-06, 'max_iter': 100, 'penalty': 'l1', 'solver': 'cgd', 'C': 1.0}; Test_score: 0.301029995663981; Train_score: 0.301029995663981; Time: 0.5177769660949707;
Model: LogisticRegression; Parameters: {'tol': 1e-06, 'max_iter': 100, 'penalty': 'l2', 'solver': 'bfgs', 'C': 1.0}; Test_score: 0.03467372215888777; Train_score: 0.040084656014095; Time: 8.65127698580424;
Model: LogisticRegression; Parameters: {'tol': 1e-06, 'max_iter': 100, 'penalty': 'enet', 'solver': 'cgd', 'C': 1.0, 'l1_ratio': 0.5}; Test_score: 0.301029995663981; Train_score: 0.301029995663981; Time: 0.4305996100107829;

Grid Search Selected Model
LogisticRegression; Parameters: {'solver': 'bfgs', 'penalty': 'l2', 'max_iter': 100, 'C': 1.0, 'tol': 1e-06}; Test_score: 0.03467372215888777; Train_score: 0.040084656014095; Time: 8.65127698580424;

Testing Model - RandomForestClassifier

Model: RandomForestClassifier; Parameters: {'max_features': 'max', 'max_leaf_nodes': 32, 'max_depth': 5, 'min_samples_leaf': 1, 'min_info_gain': 0.0, 'nbins': 32}; Test_score: 0.6912644770548286; Train_score: 0.018363257686033533; Time: 0.5188576380411783;
Model: RandomForestClassifier; Parameters: {'max_features': 'auto', 'max_leaf_nodes': 64, 'max_depth': 4, 'min_samples_leaf': 2, 'min_info_gain': 0.0, 'nbins': 32}; Test_score: 0.04780020077573837; Train_score: 0.03799784570322354; Time: 0.6012808481852213;
Model: RandomForestClassifier; Parameters: {'max_features': 'auto', 'max_leaf_nodes': 32, 'max_depth': 4, 'min_samples_leaf': 2, 'min_info_gain': 0.0, 'nbins': 32}; Test_score: 0.1121156175279119; Train_score: 0.036462521024479964; Time: 0.6061913967132568;
Model: RandomForestClassifier; Parameters: {'max_features': 'max', 'max_leaf_nodes': 64, 'max_depth': 4, 'min_samples_leaf': 2, 'min_info_gain': 0.0, 'nbins': 32}; Test_score: 0.32044589382468697; Train_score: 0.026115669482827335; Time: 0.5158038934071859;
Model: RandomForestClassifier; Parameters: {'max_features': 'auto', 'max_leaf_nodes': 1000, 'max_depth': 6, 'min_samples_leaf': 2, 'min_info_gain': 0.0, 'nbins': 32}; Test_score: 0.10587022372781703; Train_score: 0.03147628534489167; Time: 0.6188173294067383;

Grid Search Selected Model
RandomForestClassifier; Parameters: {'n_estimators': 10, 'max_features': 'auto', 'max_leaf_nodes': 64, 'sample': 0.632, 'max_depth': 4, 'min_samples_leaf': 2, 'min_info_gain': 0.0, 'nbins': 32}; Test_score: 0.04780020077573837; Train_score: 0.03799784570322354; Time: 0.6012808481852213;

Testing Model - NaiveBayes

Model: NaiveBayes; Parameters: {'alpha': 0.01}; Test_score: 49.545500338212236; Train_score: 48.0569530726675; Time: 0.32554197311401367;
Model: NaiveBayes; Parameters: {'alpha': 1.0}; Test_score: 48.6479075941986; Train_score: 48.543435159082904; Time: 0.3659666379292806;
Model: NaiveBayes; Parameters: {'alpha': 10.0}; Test_score: 48.274806620868695; Train_score: 48.7333732981366; Time: 0.36309019724527997;

Grid Search Selected Model
NaiveBayes; Parameters: {'alpha': 10.0, 'nbtype': 'auto'}; Test_score: 48.274806620868695; Train_score: 48.7333732981366; Time: 0.36309019724527997;

Final Model

LogisticRegression; Best_Parameters: {'solver': 'bfgs', 'penalty': 'l2', 'max_iter': 100, 'C': 1.0, 'tol': 1e-06}; Best_Test_score: 0.03467372215888777; Train_score: 0.040084656014095; Time: 8.65127698580424;

Out[1]:

	model_type	avg_score	avg_train_score	avg_time	score_std	score_train_std
1	LogisticRegression	0.03467372215888777	0.040084656014095	8.65127698580424	0.0027533715028225498	0.0017338790478024836
2	RandomForestClassifier	0.04780020077573837	0.03799784570322354	0.6012808481852213	0.007854641111027778	0.006098402782038309
3	LogisticRegression	0.058747133750518495	0.030520459056835366	10.177567720413208	0.049502886401589015	0.007393253247850581
4	RandomForestClassifier	0.10587022372781703	0.03147628534489167	0.6188173294067383	0.11364772306051597	0.005364074296552674
5	RandomForestClassifier	0.1121156175279119	0.036462521024479964	0.6061913967132568	0.13038154999087198	0.008716486006951588
6	LogisticRegression	0.301029995663981	0.301029995663981	0.5177769660949707	0.0	0.0
7	LogisticRegression	0.301029995663981	0.301029995663981	0.4305996100107829	0.0	0.0
8	RandomForestClassifier	0.32044589382468697	0.026115669482827335	0.5158038934071859	0.3207862599458925	0.0026885404517372454
9	RandomForestClassifier	0.6912644770548286	0.018363257686033533	0.5188576380411783	0.36540898419926204	0.0023180986540416245
10	NaiveBayes	48.274806620868695	48.7333732981366	0.36309019724527997	1.2913814331123625	0.6408280844475029
11	NaiveBayes	48.6479075941986	48.543435159082904	0.3659666379292806	1.828739911336018	0.87773409521784
12	NaiveBayes	49.545500338212236	48.0569530726675	0.32554197311401367	1.050975009868308	0.5687428669096175

Rows: 1-12 | Columns: 8