New answers tagged training
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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. ...
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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 ...
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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 ...
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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 ...
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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 * ( ...
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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 ...
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5
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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 ...
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4
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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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