class_docstr: This module helps in fitting to all the classification algorithms that are available in Scikit-learn
  Parameters
  ----------
  verbose : int, optional (default=0)
      For the liblinear and lbfgs solvers set verbose to any positive
      number for verbosity.
  ignore_warnings : bool, optional (default=True)
      When set to True, the warning related to algorigms that are not able to run are ignored.
  custom_metric : function, optional (default=None)
      When function is provided, models are evaluated based on the custom evaluation metric provided.
  prediction : bool, optional (default=False)
      When set to True, the predictions of all the models models are returned as dataframe.
  classifiers : list, optional (default="all")
      When function is provided, trains the chosen classifier(s).
  
  Examples
  --------
  >>> from lazypredict.Supervised import LazyClassifier
  >>> from sklearn.datasets import load_breast_cancer
  >>> from sklearn.model_selection import train_test_split
  >>> data = load_breast_cancer()
  >>> X = data.data
  >>> y= data.target
  >>> X_train, X_test, y_train, y_test = train_test_split(X, y,test_size=.5,random_state =123)
  >>> clf = LazyClassifier(verbose=0,ignore_warnings=True, custom_metric=None)
  >>> models,predictions = clf.fit(X_train, X_test, y_train, y_test)
  >>> model_dictionary = clf.provide_models(X_train,X_test,y_train,y_test)
  >>> models
  | Model                          |   Accuracy |   Balanced Accuracy |   ROC AUC |   F1 Score |   Time Taken |
  |:-------------------------------|-----------:|--------------------:|----------:|-----------:|-------------:|
  | LinearSVC                      |   0.989474 |            0.987544 |  0.987544 |   0.989462 |    0.0150008 |
  | SGDClassifier                  |   0.989474 |            0.987544 |  0.987544 |   0.989462 |    0.0109992 |
  | MLPClassifier                  |   0.985965 |            0.986904 |  0.986904 |   0.985994 |    0.426     |
  | Perceptron                     |   0.985965 |            0.984797 |  0.984797 |   0.985965 |    0.0120046 |
  | LogisticRegression             |   0.985965 |            0.98269  |  0.98269  |   0.985934 |    0.0200036 |
  | LogisticRegressionCV           |   0.985965 |            0.98269  |  0.98269  |   0.985934 |    0.262997  |
  | SVC                            |   0.982456 |            0.979942 |  0.979942 |   0.982437 |    0.0140011 |
  | CalibratedClassifierCV         |   0.982456 |            0.975728 |  0.975728 |   0.982357 |    0.0350015 |
  | PassiveAggressiveClassifier    |   0.975439 |            0.974448 |  0.974448 |   0.975464 |    0.0130005 |
  | LabelPropagation               |   0.975439 |            0.974448 |  0.974448 |   0.975464 |    0.0429988 |
  | LabelSpreading                 |   0.975439 |            0.974448 |  0.974448 |   0.975464 |    0.0310006 |
  | RandomForestClassifier         |   0.97193  |            0.969594 |  0.969594 |   0.97193  |    0.033     |
  | GradientBoostingClassifier     |   0.97193  |            0.967486 |  0.967486 |   0.971869 |    0.166998  |
  | QuadraticDiscriminantAnalysis  |   0.964912 |            0.966206 |  0.966206 |   0.965052 |    0.0119994 |
  | HistGradientBoostingClassifier |   0.968421 |            0.964739 |  0.964739 |   0.968387 |    0.682003  |
  | RidgeClassifierCV              |   0.97193  |            0.963272 |  0.963272 |   0.971736 |    0.0130029 |
  | RidgeClassifier                |   0.968421 |            0.960525 |  0.960525 |   0.968242 |    0.0119977 |
  | AdaBoostClassifier             |   0.961404 |            0.959245 |  0.959245 |   0.961444 |    0.204998  |
  | ExtraTreesClassifier           |   0.961404 |            0.957138 |  0.957138 |   0.961362 |    0.0270066 |
  | KNeighborsClassifier           |   0.961404 |            0.95503  |  0.95503  |   0.961276 |    0.0560005 |
  | BaggingClassifier              |   0.947368 |            0.954577 |  0.954577 |   0.947882 |    0.0559971 |
  | BernoulliNB                    |   0.950877 |            0.951003 |  0.951003 |   0.951072 |    0.0169988 |
  | LinearDiscriminantAnalysis     |   0.961404 |            0.950816 |  0.950816 |   0.961089 |    0.0199995 |
  | GaussianNB                     |   0.954386 |            0.949536 |  0.949536 |   0.954337 |    0.0139935 |
  | NuSVC                          |   0.954386 |            0.943215 |  0.943215 |   0.954014 |    0.019989  |
  | DecisionTreeClassifier         |   0.936842 |            0.933693 |  0.933693 |   0.936971 |    0.0170023 |
  | NearestCentroid                |   0.947368 |            0.933506 |  0.933506 |   0.946801 |    0.0160074 |
  | ExtraTreeClassifier            |   0.922807 |            0.912168 |  0.912168 |   0.922462 |    0.0109999 |
  | CheckingClassifier             |   0.361404 |            0.5      |  0.5      |   0.191879 |    0.0170043 |
  | DummyClassifier                |   0.512281 |            0.489598 |  0.489598 |   0.518924 |    0.0119965 |
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docstr: This function returns all the model objects trained in fit function.
  If fit is not called already, then we call fit and then return the models.
  Parameters
  ----------
  X_train : array-like,
      Training vectors, where rows is the number of samples
      and columns is the number of features.
  X_test : array-like,
      Testing vectors, where rows is the number of samples
      and columns is the number of features.
  y_train : array-like,
      Training vectors, where rows is the number of samples
      and columns is the number of features.
  y_test : array-like,
      Testing vectors, where rows is the number of samples
      and columns is the number of features.
  Returns
  -------
  models: dict-object,
      Returns a dictionary with each model pipeline as value 
      with key as name of models.
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code: def provide_models(self, X_train, X_test, y_train, y_test):
      """
      This function returns all the model objects trained in fit function.
      If fit is not called already, then we call fit and then return the models.
      Parameters
      ----------
      X_train : array-like,
          Training vectors, where rows is the number of samples
          and columns is the number of features.
      X_test : array-like,
          Testing vectors, where rows is the number of samples
          and columns is the number of features.
      y_train : array-like,
          Training vectors, where rows is the number of samples
          and columns is the number of features.
      y_test : array-like,
          Testing vectors, where rows is the number of samples
          and columns is the number of features.
      Returns
      -------
      models: dict-object,
          Returns a dictionary with each model pipeline as value
          with key as name of models.
      """
      if len(self.models.keys()) == 0:
          self.fit(X_train, X_test, y_train, y_test)
  
      return self.models
  
imports: import numpy
  import pandas
  from tqdm import tqdm
  import datetime
  import time
  from sklearn.pipeline import Pipeline
  from sklearn.impute import SimpleImputer, MissingIndicator
  from sklearn.preprocessing import StandardScaler, OneHotEncoder, OrdinalEncoder
  from sklearn.compose import ColumnTransformer
  from sklearn.utils import all_estimators
  from sklearn.base import RegressorMixin
  from sklearn.base import ClassifierMixin
  from sklearn.metrics import accuracy_score, balanced_accuracy_score, roc_auc_score, f1_score, r2_score, mean_squared_error
  import warnings
  import xgboost
  import lightgbm
label_desc: Use image node_id 3 for calling the LazyClassifier obj's underlying member method code with example usage: obj.provide_models(X_train, X_test, y_train, y_test) and returns: self