class_docstr: Create a new model.
  
  Parameters
  ----------
  specs : dict
      The model specifications obtained by reading the ``model.yml``
      file.
  
  Attributes
  ----------
  specs : dict
      The model specifications.
  X_train : pandas.DataFrame
      Training features in matrix format.
  X_test  : pandas.Series
      Testing features in matrix format.
  y_train : pandas.DataFrame
      Training labels in vector format.
  y_test  : pandas.Series
      Testing labels in vector format.
  algolist : list
      Algorithms to use in training.
  estimators : dict
      Dictionary of estimators (key: algorithm)
  importances : dict
      Feature Importances (key: algorithm)
  coefs : dict
      Coefficients, if applicable (key: algorithm)
  support : dict
      Support Vectors, if applicable (key: algorithm)
  preds : dict
      Predictions or labels (keys: algorithm, partition)
  probas : dict
      Probabilities from classification (keys: algorithm, partition)
  metrics : dict
      Model evaluation metrics (keys: algorith, partition, metric)
  
  Raises
  ------
  KeyError
      Model specs must include the key *algorithms*, which is
      stored in ``algolist``.
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code: def __str__(self):
      return self.name
  
imports: from alphapy.estimators import scorers
  from alphapy.estimators import xgb_score_map
  from alphapy.features import feature_scorers
  from alphapy.frame import read_frame
  from alphapy.frame import write_frame
  from alphapy.globals import Encoders
  from alphapy.globals import ModelType
  from alphapy.globals import Objective
  from alphapy.globals import Partition, datasets
  from alphapy.globals import PSEP, SSEP, USEP
  from alphapy.globals import SamplingMethod
  from alphapy.globals import Scalers
  from alphapy.utilities import get_datestamp
  from alphapy.utilities import most_recent_file
  from copy import copy
  from datetime import datetime
  import itertools
  import joblib
  from keras.models import load_model
  import logging
  import numpy
  import pandas
  from sklearn.calibration import CalibratedClassifierCV
  from sklearn.linear_model import LogisticRegression
  from sklearn.linear_model import RidgeCV
  from sklearn.metrics import accuracy_score
  from sklearn.metrics import auc
  from sklearn.metrics import average_precision_score
  from sklearn.metrics import balanced_accuracy_score
  from sklearn.metrics import brier_score_loss
  from sklearn.metrics import classification_report
  from sklearn.metrics import cohen_kappa_score
  from sklearn.metrics import confusion_matrix
  from sklearn.metrics import explained_variance_score
  from sklearn.metrics import f1_score
  from sklearn.metrics import log_loss
  from sklearn.metrics import mean_absolute_error
  from sklearn.metrics import mean_squared_error
  from sklearn.metrics import mean_squared_log_error
  from sklearn.metrics import median_absolute_error
  from sklearn.metrics import precision_score
  from sklearn.metrics import r2_score
  from sklearn.metrics import recall_score
  from sklearn.metrics import roc_auc_score
  from sklearn.metrics import roc_curve
  from sklearn.metrics.cluster import adjusted_rand_score
  from sklearn.model_selection import cross_val_score
  from sklearn.model_selection import train_test_split
  import sys
  import yaml
label_desc: Use image node_id 2 for calling the Model obj's underlying member method code with example usage: obj.__str__() and returns: self