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1 vote

How can someone build a dataset for a "propensity to purchase" model?

This is a long post with many questions, but I will try my best to answer. Let's start with the terminology: when we say "propensity model", usually we mean predicting future events (e.g. ...
  • 587
0 votes

unbalanced data on train set and test set

It is often useful to balance a training dataset. For example, if the model learns a decision boundary, that decision boundary will then learn to separate different categories based more on features ...
0 votes

unbalanced data on train set and test set

It's also possible to decrease the learning step when updating weights learned from the majority class, and/or increase the learning step when updating weights learned from the minority class. See ...
0 votes

unbalanced data on train set and test set

If your dataset is sufficiently large and you might want to reduce its size for performance reasons anyways, you could do undersampling of label 1. However, if you only have a limited amount of data ...
  • 126
0 votes
Accepted

Evaluate a Recommender System based on the data between two months

Because tool A and B might result in recommendations with different numbers of items, using ratio is more suitable than the actual score. calculate hit ratio for each one eg. hit ratio = 1 * ( ...
  • 26
2 votes
Accepted

How do you retrain a model as new data comes in?

This is a decision you have to make depending on your model and use case. Here are a few points that you might find useful: What you are referring to is called online learning. This is the idea that ...
  • 1,209
5 votes

Do model training pipeline should run on dev, staging and production environment?

Yes - Production data should be used. The highest quality, newest data should be used to train a machine learning model. Typically, new data is used to fine-tune existing models. No - Training should ...
4 votes

Do model training pipeline should run on dev, staging and production environment?

Yes! You can take a dump of production data, merge with existing training data (with all processing steps) and retrain (as many number of experiments desired) your model. But before you do that, it ...

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