mlinsights: tricky scikit-learn

mlinsights implements functions to get insights on machine learned models or various kind of transforms to help manipulating data in a single pipeline. It implements QuantileLinearRegression which trains a linear regression with L1 norm non-linear correlation based on decision trees, QuantileMLPRegressor which is a modification of scikit-learn’s MLPRegressor which trains a multi-layer perceptron with L1 norm…

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Short example:

<<<

from sklearn.datasets import load_diabetes
from sklearn.linear_model import LinearRegression
from mlinsights.mlmodel import QuantileLinearRegression

data = load_diabetes()
X, y = data.data, data.target

clq = QuantileLinearRegression()
clq.fit(X, y)
print(clq.coef_)

clr = LinearRegression()
clr.fit(X, y)
print(clr.coef_)

>>>

    [   5.759 -301.117  464.043  385.354 -805.267  425.193  110.316  245.851
      738.518   35.686]
    [ -10.01  -239.816  519.846  324.385 -792.176  476.739  101.043  177.063
      751.274   67.627]

This documentation was generated with scikit-learn version…

<<<

from sklearn import __version__

print(__version__)

>>>

    1.6.dev0

Source are available at sdpython/mlinsights.

Older versions

• 0.5.1

• 0.5.0