4 ms·
Because they are so much similar that it is much easier to implement them as part of one mechanism. For example: * Apply a (say, SVM) classification model to e
by asavinov 8y ago
Because they are so much similar that it is much easier to implement them as part of one mechanism. For example:
* Apply a (say, SVM) classification model to each object (row) by producing a new column
* Generate a new column as a difference between its values and its average over all rows.
In both cases, you produce a new column (=feature) by applying some transformation. Also, in both cases, you need to find parameters of this transformation from the data. In the first case, by training SVM model. In the second case, by find the average value.
Conclusion: there is no essential difference between defining/training a feature and a ML model.
- pplonski86 8y agoI think it depends on your context. From theoretical point of view you can treat both (feature engineering and ML) as transformation defined by some function with parameters. From practical point of view, they can differ a lot. Imagine, you would like to handle in a different way: feature transformation that can be done on 1M rows and 2 columns in 5 seconds, and training of ML model that can take 7 days to train.