Comments (1)
The resulting features could definitely be used as input for another algorithm as well!
Concerning the classification problem: The model is supposed to be used for problems with a single target variable. This means that you could in principle also use it for a binary classification problem (i.e. if your labels are only 0 and 1), but it doesn't make sense for multiclass problems, unless you frame them as a one-vs-rest binary classification task for each of the classes - but this will probably get pretty messy if you have more than a handful of classes.
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Related Issues (20)
- Data validation error when using Buckingham's Pi Theorem on Classification task HOT 1
- Is it possible to use autofeat without exceeding memory of the system? HOT 3
- possible point for verification HOT 3
- How to transform new data? HOT 1
- Speed up tranform() HOT 6
- MemoryError: Unable to allocate 2.05 GiB for an array with shape (501, 550174) and data type float64
- pandas corr is too slow; use numpy instead HOT 1
- Correlation matrix can have inconsistent column and row names HOT 1
- Allow user to pass dict of Pint objects/ureg
- Input contains NaN, infinity or a value too large for dtype('float32') on fit_transform HOT 2
- ufunc '_lambdifygenerated' did not contain a loop with signature matching types (<class 'numpy.dtype[float32]'>, <class 'numpy.dtype[float32]'>) -> None HOT 6
- How to choose sin(x) and cos(x) etl. as features? HOT 1
- Scaling and Autofeat HOT 2
- [enhancement] add predict_proba for classifiers HOT 11
- TypeError: unsupported operand type(s) for |: 'type' and 'NoneType' HOT 1
- ValueError: Input X contains NaN. HOT 6
- Documentation Enhancement for getting model, features, coefficients HOT 1
- AutoFeatLight.fit_trasnform() take 2 positional arguments 3 given HOT 2
- Reproducibility issue HOT 1
- In toydata, autofeat finds the correct function (square) only under some circumstances HOT 1
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