Scikit-Learn's linear regression extended with p-values.
"... the null hypothesis is never proved or established, but is possibly disproved, in the course of experimentation. Every experiment may be said to exist only to give the facts a chance of disproving the null hypothesis." - R. A. Fisher
People from R background are familiar with hypothesis testing and p-values whereas ones from Python's scikit-learn background haven't heard of them. That's why I just made this script, which is an extension of scikit-learn's LinearRegression, it can also be extended to Lasso and Ridge regressions respectively.
A quick example:
from p_linear_regression import PLinearRegression
plr = PLinearRegression()
plr.fit(X_train, y_train)
y_pred = plr.predict(X_test)
print(plr.summary)Output for the diabetes dataset from sklearn looks like this:
coefficients standard Errors t statistic p values
0 37.900314 68.934688 0.549800 5.828141e-01
1 -241.966248 68.468840 -3.533962 4.654885e-04
2 542.425753 76.826436 7.060405 9.272139e-12
3 347.708305 71.252628 4.879937 1.628348e-06
4 -931.461261 450.477090 -2.067722 3.941565e-02
5 518.044055 363.566096 1.424896 1.550964e-01
6 163.403535 232.663793 0.702316 4.829584e-01
7 275.310038 185.125003 1.487158 1.378919e-01
8 736.189098 192.157851 3.831168 1.516492e-04
9 48.671125 73.305207 0.663952 5.071672e-01
There's also a demo.ipynb along with this repository for demonstration purposes.
critiques, comments and suggestions are always welcome.