Machine Learning Techniques or Classifiers for Models

Use machine learning techniques to perform Model building or Model fitting. Model fitting is a measure of how well a machine learning model generalizes to similar data to that on which it was trained. A well-fitted model produces more accurate outcomes. Compare the outcomes to real, observed values of the target variable to determine their accuracy.

Most of the above techniques are from standard scikit learn classifiers, except for Weight of Evidence Logistic Regression (WOELR), which is specific to AIF. The following techniques are supported:

·        Weight of Evidence Logistic Regression (WOE)

·        Logistic Regression (LR)

·        Xtreme Gradient Boosting (XGB)

·        Gradient Boosting (GB)

·        Ada Boost (AD)

·        Random Forest (RF)

·        Naive Bayes (NB)

·        Multi-Layer Perception (MLP)