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:])
Tries to minimize time-to-first-model
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:
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
- More customizability
- Tree-based data (being able to exclude grouped variables quickly)