PredictionStacking

PredictionStacking

The function calculates the final PD based on the models obtained with a stacking scheme.

Parameters:

• models_dict: dict
  A dictionary with LogitModel instances.
• x_data: pd.DataFrame
  A pandas DataFrame with data of clients to calculate PD.
• model: ModelStacking
  A Logistic Regression model obtained using the ModelStacking function.
• logprob: bool
  An indicator to calculate the logarithm of probabilities.

Returns:

• pred: pd.Series
  A predicted PD for a client using x_data and the stacking model.

Exceptions:

• TypeError:
  Raised if models_dict is not a dictionary
  Raised if x_data is not a pandas DataFrame object
  Raised if the model parameter is not a Logistic Regression object
  Raised if logprob is not logical
  

Example:

from combat.combat import PredictionStacking
import pandas as pd

# Sample input data
models_dict = {1: model1 # LogitModel instance
              , 2: model2 # LogitModel instance
              , 3: model3 # LogitModel instance
              }
x_data = your_data
stacked_model = your_stacked_model

# Calculate the final PD using stacking
final_pd = PredictionStacking(models_dict, x_data, stacked_model, logprob=False)
print("Final PD:", final_pd)