robinsingh1/xtoy

Go from 'X' to 'y' without effort.

XtoY

Go from 'X' to 'y' without effort.

from sklearn.datasets import load_digits
from xtoy.toys import Toy
X, y = load_digits(return_X_y=True)
toy = Toy()
toy.fit(X[:900], y[:900])
toy.predict(X[900:])

Guarantee

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.

Test driven

Quality guarantee by testing code changes, with loss measurements on lots of data problems.

Features

• ✓ 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

Roadmap

• Considering everything as sparse data, or
• Working with huge data by considering columns at a time
• Better classifier settings
• More customizability
• Tree-based data (being able to exclude grouped variables quickly)