human learn: titanic
Human-Learn contains scikit-learn compatible tools that should make it easier to construct and benchmark rule based systems that are designed by humans. You can also use it in combination with ML models. In this series of videos we'll demonstrate some of the features.
Before running the code, make sure you've installed the package.
python -m pip install human-learn
fare_based function defined below can be used to make predictions.
from hulearn.datasets import load_titanic df = load_titanic(as_frame=True) X, y = df.drop(columns=['survived']), df['survived'] def fare_based(dataf, threshold=10): return np.array(dataf['fare'] > threshold).astype(int)
In the next video we'll explore how to make it into a scikit-learn compatible classifier.
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