Application of MicrosoftML rxFastTree algorithm to Insurance
payment validations and predictions:
Logistic regression is a well-known statistical technique
that is used to model binary outcomes. It can be applied to detect root causes
of payment errors. It uses statistical measures, is highly flexible, takes any
kind of input and supports different analytical tasks. This regression folds
the effects of extreme values and evaluates several factors that affect a pair
of outcomes.
Logistic regression differs from the other Regression
techniques in the use of statistical measures. Regression is very useful to
calculate a linear relationship between a dependent and independent variable,
and then use that relationship for prediction. Errors demonstrate elongated
scatter plots in specific categories. Even when the errors come with different
error details in the same category, they can be plotted with correlation. This
technique is suitable for specific error categories from an account.
One advantage of logistic regression is that the algorithm
is highly flexible, taking any kind of input, and supports several different
analytical tasks:
-
Use demographics to make predictions about
outcomes, such as probability of defaulting payments.
-
Explore and weight the factors that contribute
to a result. For example, find the factors that influence customers to make a
repeat late payment.
-
Classify claims, payments, or other objects that
have many attributes.
Microsoft ML rxFastTree algorithm is also an example.
The gradient boost
algorithm for rxFastTree is possible with several loss functions including the
squared loss function.
The algorithm for the least squares regression can be
written as :
1. Set the initial approximation
2. For a set of successive increments or boosts each based
on the preceding iterations, do
3. Calculate the new residuals
4. Find the line of search by aggregating and minimizing the
residuals
5. Perform the boost along the line of search
6. Repeat 3,4,5 for each of 2.
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