text: Title: I have confusion with final ensemble model.
  Body: My constractor: 
  `automl = autosklearn.classification.AutoSklearnClassifier( 
              time_left_for_this_task=3600,
              memory_limit=3072,
              ensemble_size=10,
              ensemble_nbest=10,
              max_models_on_disc=10,
              delete_tmp_folder_after_terminate=False,
  )`
  Below is my model's sprint_statistics
  Number of target algorithm runs: 17\n  
  Number of successful target algorithm runs: 4\n  
  Number of crashed target algorithm runs: 2\n  
  Number of target algorithms that exceeded the time limit: 9\n  
  Number of target algorithms that exceeded the memory limit: 2\n'
  
  
  output of `show_model()` :
  `"[(1.000000, SimpleClassificationPipeline({'balancing:strategy': 'weighting', 'classifier:__choice__': 'random_forest', 'data_preprocessing:categorical_transformer:categorical_encoding:__choice__': 'no_encoding', 'data_preprocessing:categorical_transformer:category_coalescence:__choice__': 'minority_coalescer', 'data_preprocessing:numerical_transformer:imputation:strategy': 'mean', 'data_preprocessing:numerical_transformer:rescaling:__choice__': 'robust_scaler', 'feature_preprocessor:__choice__': 'select_rates_classification', 'classifier:random_forest:bootstrap': 'False', 'classifier:random_forest:criterion': 'gini', 'classifier:random_forest:max_depth': 'None', 'classifier:random_forest:max_features': 0.23832696118792362, 'classifier:random_forest:max_leaf_nodes': 'None', 'classifier:random_forest:min_impurity_decrease': 0.0, 'classifier:random_forest:min_samples_leaf': 1, 'classifier:random_forest:min_samples_split': 16, 'classifier:random_forest:min_weight_fraction_leaf': 0.0, 'data_preprocessing:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction': 0.035949803138524174, 'data_preprocessing:numerical_transformer:rescaling:robust_scaler:q_max': 0.7356350569414665, 'data_preprocessing:numerical_transformer:rescaling:robust_scaler:q_min': 0.2902106911441806, 'feature_preprocessor:select_rates_classification:alpha': 0.3610975428987517, 'feature_preprocessor:select_rates_classification:score_func': 'mutual_info_classif'},\ndataset_properties={\n  'task': 2,\n  'sparse': False,\n  'multilabel': False,\n  'multiclass': True,\n  'target_type': 'classification',\n  'signed': False})),\n]"`
  
  My question is why I got only one algorithm/configuration in final ensemble model if my successful target algorithms are 4 and ensemble size are 10? My final model should be ensemble of 4 configurations, right?