Question regarding simultaneous objectives

[robinvanschaik]

First of all, thank you for making the code for the loss function, metrics & example notebooks open source.

I am looking forward to trying this approach out. I have one question that I am a bit confused about.

The paper mentions that the model structure, in combination with the loss function used, allows for one model to capture both objectives: churn prediction and remaining lifetime value prediction.

First, it is capable of predicting the churn probability and LTV value simultane-
ously. It reduces the engineering complexity of building a two-stage model (Vanderveld et al., 2016)
— a binary classification model to predict repeat purchase propensity, followed by a regression model
to predict the LTV of returning customers predicted in stage 1.

Indeed, the loss function returns a composite of both churn & remaining value losses:

  return classification_loss + regression_loss

However, the notebooks are split up in two, one for each separate objective.

Can one trained model indeed perform both tasks: the notebooks are split up to illustrate a comparison between the churn and remaining value tasks to two separate models?

Or are did I completely misinterpret the paper? In which it is meant that the type of model can perform both tasks, but you need two trained models: one fine-tuned for each task.

Thanks, and I am looking forward to trying this out. :)

[TerenceLiu4444]

One trained model can perform both tasks. The notebooks are separated into classification and regression for illustration purpose.

For churn prediction, you can use
y_pred = K.sigmoid(logits[..., :1]).numpy().flatten(). For average ltv, you can use

  logits = model.predict(x=x_eval, batch_size=1024)
  y_pred = ltv.zero_inflated_lognormal_pred(logits).numpy().flatten()

For remaining values, you can use

  loc = logits[..., 1:2]
  scale = tf.keras.backend.softplus(logits[..., 2:])
  preds = (
      tf.keras.backend.exp(loc + 0.5 * tf.keras.backend.square(scale)))

[robinvanschaik]

Thank you, @TerenceLiu4444.

That's clear.