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You want to update an algorithm when:
You want the model to evolve as data streams through infrastructure
The underlying data source is changing
There is a case of non-stationarity
Eigenvectors are for understanding linear transformations. In data analysis, we usually calculate the eigenvectors for a correlation or covariance matrix. Eigenvectors are the directions along which a particular linear transformation acts by flipping, compressing or stretching.
Resampling is done in one of these cases:
Estimating the accuracy of sample statistics by using subsets of
accessible data or drawing randomly with replacement from a set of data
points Substituting labels on data points when performing significance tests
Validating models by using random subsets (bootstrapping, cross
validation.
Selection bias, in general, is a problematic situation in which error is introduced due to a non-random population sample.
- Selection bias
- Under coverage bias
- Survivorship bias
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