Please enable JavaScript.
Coggle requires JavaScript to display documents.
Deloitte’s Data Analytics (Model Development (Training versus…
Deloitte’s Data Analytics
Model Perfomance
Predictive Power
Ability to generalise the rules it has learned from the training data set to a new one
Graph of two histograms
Confusion Matrix
Additional measure of predictive power
Goodness of Fit
Contained one response variable and only one explanatory variable
Model Development
Logistic regression
Successful and transparent ways to do the required binary classification to “good” and “bad”
Training versus generalisation error
The data will be split into two parts
The first part will be used for extracting the correct coefficients by minimising the error between model output and observed output
The second part is used for testing the “generalisation” ability of the model
Reject inference
This implies that the model is not truly representative
Variable selection
Requires a critical view and understanding on the variables and a selection of the most significant ones
Classification
Distinguish the “good” applicants
The statistical models is required to find the separating line distinguishing the two categories
Information Value
Based on the idea that we perform a univariate analysis
Measure of how significant is the discriminatory power of a variable
Model Refinements
Absence of interactions among explanatory variables
No terms mixing the variables
Linearity in the explanatory variables
Inside the exponentials there are no higher-order terms
Model Interpretation
x is a Boolean variable
Obtain the two equations
Provide further guidance by giving the impact of each individual explanatory variable
One explanatory variable