Comments (2)
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)))
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Thank you, @TerenceLiu4444.
That's clear.
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Related Issues (6)
- In ZILN loss network, the three activation function are sigmoid, identity and softplus. HOT 1
- Loss calculation HOT 1
- Why the ltv prediction part use probabilitity prediction multiply expectation as the final ltv prediction? HOT 2
- How are LTV distributions calculated? HOT 1
- Upgrade to scikit-learn as opposed to sklearn
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