By: Ram on Jun 19, 2020
Some of the commonly used Model Performance Statistics for Binary Predictive Models are:
In this blog, we will show the steps involved in calculating concordance %.
In a binary predictive modeling scenario, we have two important variables: Observed Target Variable and Predicted Outcome Variable. For example, in a credit risk default scenario, we have created a target variable that takes value 1 (if a customer has defaulted) and 0. This is our observed target variable. Based on the predictive model especially when a logistic regression technique is used, we get predicted probabilities which are the Predicted Outcome Variable.
Compare Observed Target Variable values and Predicted Outcome Variable values to assess the efficacy of the predictive model.
In concordance, predicted probabilities of Observed Target variable value 1 is compared against the predicted probabilities of Observed Target Variable value 0. If predicted probabilities value for observation with Observed Target variable 1 is higher than the predicted probabilities value for observation with Observed Target variable 0, then it is concordant otherwise discordance. When the predicted probabilities for observed target variable value 1 and 0 is equal, it is called tied.
# of Pairs: 9*10=90% Concordance: 56/90 => 62%% Discordance: 34/90 => 38%% Tied: 0/90 => 0%