How does Smart Predict Create Assigned Bins?

While applying a classification or regression predictive model to an application dataset, you can require to get the statistics information on Assigned bins. But what are Assigned bins and how should they be leveraged?

During the training step, Smart Predict uses past observations compiled in a training dataset to create a predictive model.

For a classification predictive model: Smart Predict associates to each observation (customer, product, etc…) a probability that an event (target) occurs. Then, it uses this probability to group the list of observations, ranged in decreasing order from the most probable to the least probable in 10 bins (or groups). Each bin represents 10% of those observations and in each bin, the observations have the same level of probability.
For a regression predictive model: Smart Predict associates to each observation a predicted value. Based on this value, it groups the list of observations ranged from the highest to the lowest predicted value in 10 bins (or groups). Each bin represents 10% of those observations and in each bin, the observations have the same value or range of values.

During the application step, Smart Predict refers to the bins defined in the training step to assign the current observations from the application dataset to the relevant bin. It compares each value obtained by the predictive model with the limits of each Assigned bin defined in the training step. Then it assigns each observation to the relevant bin.