update doc

    Random forest(rf), MultitaskDNN(tf),

    RobustMultitaskDNN(tf_robust),

    Logistic regression(logreg), IRV(irv)

    Graph convolution(graphconv)

    Graph convolution(graphconv),

    Xgboost classifier(xgb_classifier)

on datasets: muv, pcba, tox21, sider, toxcast, clintox, hiv

Giving regression performances of:

    MultitaskDNN(tf_regression),

    Random forest(rf_regression),

    Graph convolution regression(graphconvreg)

    Graph convolution regression(graphconvreg),

    Xgboost regression(xgb_regression)

on datasets: delaney(ESOL), nci, kaggle, pdbbind,

             qm7, qm7b, qm9, chembl, sampl(FreeSolv)

      qm7, qm7b, qm9, sampl

  model: string,  optional (default='tf')

      choice of which model to use, should be: rf, tf, tf_robust, logreg,

      irv, graphconv, tf_regression, rf_regression, graphconvreg

      irv, graphconv, xgb_classifier, tf_regression, rf_regression, 

      graphconvreg, xgb_regression

  split: string,  optional (default=None)

      choice of splitter function, None = using the default splitter

  out_path: string, optional(default='.')

    featurizer = 'GraphConv'

    n_features = 75

  elif model in [

      'tf', 'tf_robust', 'logreg', 'rf', 'irv', 'tf_regression', 'rf_regression'

      'tf', 'tf_robust', 'logreg', 'rf', 'irv', 'tf_regression', 'rf_regression',

      'xgb_classifier', 'xgb_regression'

  ]:

    featurizer = 'ECFP'

    n_features = 1024

      number of features, or length of binary fingerprints

  model: string,  optional (default='tf')

      choice of which model to use, should be: rf, tf, tf_robust, logreg,

      irv, graphconv

      irv, graphconv, xgb_classifier

  test: boolean

      whether to calculate test_set performance

      number of features, or length of binary fingerprints

  model: string,  optional (default='tf_regression')

      choice of which model to use, should be: tf_regression, graphconvreg,

      rf_regression

      rf_regression, xgb_regression

  test: boolean

      whether to calculate test_set performance

    model_xgb = dc.models.multitask.SingletaskToMultitask(tasks, model_builder)

    print('-------------------------------------')

    print('Start fitting by xgoost')

    print('Start fitting by xgboost')

    model_xgb.fit(train_dataset)

    # Evaluating xgboost classification model