mlinsights: tricky scikit-learn#
Links: github, documentation, README, blog
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…
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_)
>>>
[ 5.75895899 -301.11702117 464.04300247 385.35409612 -805.26720203
425.19307405 110.3164599 245.8509531 738.51804393 35.6856007 ]
[ -10.0098663 -239.81564367 519.84592005 324.3846455 -792.17563855
476.73902101 101.04326794 177.06323767 751.27369956 67.62669218]
This documentation was generated with scikit-learn version…
<<<
from sklearn import __version__
print(__version__)
>>>
1.1.1