def load_creditscoring1(cost_mat_parameters=None):
    """Load and return the credit scoring Kaggle Credit competition dataset (classification).

    The credit scoring is a easily transformable example-dependent cost-sensitive classification dataset.

    Parameters
    ----------
    cost_mat_parameters : Dictionary-like object, optional (default=None)
        If not None, must include 'int_r', 'int_cf', 'cl_max', 'n_term', 'k','lgd'

    Returns
    -------
    data : Bunch
        Dictionary-like object, the interesting attributes are:
        'data', the data to learn, 'target', the classification labels,
        'cost_mat', the cost matrix of each example,
        'target_names', the meaning of the labels, 'feature_names', the
        meaning of the features, and 'DESCR', the full description of the dataset.

    References
    ----------
    .. [1] A. Correa Bahnsen, D.Aouada, B, Ottersten,
           "Example-Dependent Cost-Sensitive Logistic Regression for Credit Scoring",
           in Proceedings of the International Conference on Machine Learning and Applications,
           , 2014.

    Examples
    --------
    Let's say you are interested in the samples 10, 25, and 50

    >>> from costcla.datasets import load_creditscoring1
    >>> data = load_creditscoring1()
    >>> data.target[[10, 17, 400]]
    array([0, 1, 0])
    >>> data.cost_mat[[10, 17, 400]]
    array([[  1023.73054104,  18750.        ,      0.        ,      0.        ],
           [   717.25781516,   6749.25      ,      0.        ,      0.        ],
           [  1004.32819923,  17990.25      ,      0.        ,      0.        ]])
    """
    module_path = dirname(__file__)
    raw_data = pd.read_csv(join(module_path, 'data', 'creditscoring1.csv.gz'), delimiter=',', compression='gzip')
    descr = open(join(module_path, 'descr', 'creditscoring1.rst')).read()

    # Exclude MonthlyIncome = nan or =0 or DebtRatio >1
    raw_data = raw_data.dropna()
    raw_data = raw_data.loc[(raw_data['MonthlyIncome'] > 0)]
    raw_data = raw_data.loc[(raw_data['DebtRatio'] < 1)]

    target = raw_data['SeriousDlqin2yrs'].values.astype(np.int)

    data = raw_data.drop(['SeriousDlqin2yrs', 'id'], 1)

    # Calculate cost_mat (see[1])
    if cost_mat_parameters is None:
        cost_mat_parameters = {'int_r': 0.0479 / 12,
                               'int_cf': 0.0294 / 12,
                               'cl_max': 25000,
                               'n_term': 24,
                               'k': 3,
                               'lgd': .75}

    pi_1 = target.mean()
    cost_mat = _creditscoring_costmat(data['MonthlyIncome'].values, data['DebtRatio'].values, pi_1, cost_mat_parameters)

    return Bunch(data=data.values, target=target, cost_mat=cost_mat,
                 target_names=['no', 'yes'], DESCR=descr,
                 feature_names=data.columns.values, name='CreditScoring_Kaggle2011')