pip install xtoy
Go from 'X' to 'y' without effort.
from sklearn.datasets import load_diabetes
from xtoy.toys import Toy
X, y = load_diabetes(return_X_y=True)
toy = Toy()
toy.fit(X[:300], y[:300])
toy.predict(X[300:])And a reasonable one at that.
Check how important each variable is:
# names of variables are numbers - only in this example - otherwise usually strings
toy.best_features_()
[(0.02541263748358529, 4),
(0.03964045497300279, 6),
(0.04000655539791701, 5),
(0.047171804294566556, 0),
(0.05355633793403717, 1),
(0.05598481754558562, 9),
(0.06349342396487742, 3),
(0.09050228976499292, 7),
(0.28327316154993126, 2),
(0.3009585170915041, 8)]For further inspection, have a look at the pipeline and how important each variable is:
# toy.best_pipeline_The goal will be to accept ANY data and come up with a "sensible" prediction.
If your dataset doesn't work (asymptotically not happening), post an issue.
Quality guarantee by testing code changes, with loss measurements on lots of data problems.
- ✓ Takes care of encoding text, categorical, dates (several features), continuous
- Considers data size (small data -> feature engineering, big data -> feature selection)
- ✓ Takes care of missing values
- ✓ Creates a model
- ✓ Optimizes model parameters
- ✓ Gives you a first prediction
- ✓ Contains a
RegexVectorizer
- More customizability
- Tree-based data (being able to exclude grouped variables quickly)