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_boston
from sklearn.linear_model import LinearRegression
from mlinsights.mlmodel import QuantileLinearRegression
data = load_boston()
X, y = data.data, data.target
clq = QuantileLinearRegression()
clq.fit(X, y)
print(clq.coef_)
clr = LinearRegression()
clr.fit(X, y)
print(clr.coef_)
>>>
[-1.38360824e-01 3.50984183e-02 3.66268177e-02 1.34105371e+00
-1.09139098e+01 4.93325545e+00 -1.85879308e-02 -1.05912067e+00
2.13237100e-01 -1.07542971e-02 -8.08288526e-01 1.10490363e-02
-3.67998974e-01]
[-1.08011358e-01 4.64204584e-02 2.05586264e-02 2.68673382e+00
-1.77666112e+01 3.80986521e+00 6.92224640e-04 -1.47556685e+00
3.06049479e-01 -1.23345939e-02 -9.52747232e-01 9.31168327e-03
-5.24758378e-01]
This documentation was generated with scikit-learn version…
<<<
from sklearn import __version__
print(__version__)
>>>
1.0.2