Question

use the model, mdl churn vs relationship, and the explanatory data, explanatory data, to predict the probability of churning. assign the predictions to the has churned column of a data frame, prediction data. remember to set the prediction type.

Answer 1

To predict the probability of churning, you would use the specified machine learning model, along with explanatory data, to generate predictions and then assign these to a 'has churned' column in a prediction DataFrame with the correct prediction type.

Step-by-step explanation:

The question relates to machine learning and data prediction using a given model named mdl churn vs relationship. To predict the probability of churning, typically, you would use the model to score a set of explanatory data. In practical terms, using a programming language like Python, you would load the model and the data, and then use a function like predict_proba if you're using libraries such as scikit-learn. It is important to select the correct prediction type, usually 'binary' for churn prediction (meaning the output will be either 'churn' or 'no churn').

The predicted probabilities of churning would be assigned to the has churned column of a new DataFrame named prediction data. The process would look something like this:

prediction_data['has churned'] = mdl_churn_vs_relationship.predict_proba(explanatory_data)[:,1]

This snippet of code assumes that you are working with Python and that the model returns the probability of the positive class (churn) as the second element, hence the [:,1].