Menu
Is it possible to have missing values in scikit-learn ? How should they be represented? I couldn't find any documentation about that.
561
k-NN and Random Forest algorithms can also support missing values. the k-NN algorithm considers the missing values by taking the majority of the K nearest values.
Decision Tree can automatically handle missing values. Decision Tree is usually robust to outliers and can handle them automatically.
Missing values are simply not supported in scikit-learn. There has been discussion on the mailing list about this before, but no attempt to actually write code to handle them.
Whatever you do, don't use NaN to encode missing values, since many of the algorithms refuse to handle samples containing NaNs.
The above answer is outdated; the latest release of scikit-learn has a class Imputer that does simple, per-feature missing value imputation. You can feed it arrays containing NaNs to have those replaced by the mean, median or mode of the corresponding feature.
165
If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
Donate Us With
