8000 DOC: Add example to linear_model.PoissonRegressor by nuka137 · Pull Request #17453 · scikit-learn/scikit-learn · GitHub
[go: up one dir, main page]
Skip to content

DOC: Add example to linear_model.PoissonRegressor #17453

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 5 commits into from
Jun 7, 2020
Merged

DOC: Add example to linear_model.PoissonRegressor #17453

Changes from all commits
Commits
Show all changes
5 commits
Select commit Hold shift + click to select a range
1f402f9
[WIP]: Add examples to linear_model.PoissonRegressor
nuka137 Jun 5, 2020
519941c
Address review
nuka137 Jun 7, 2020
c8eb89c
Address review
nuka137 Jun 7, 2020
3110ad6
Apply suggestions from code review
nuka137 Jun 7, 2020
9dc077b
Remove numpy import
nuka137 Jun 7, 2020
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
17 changes: 17 additions & 0 deletions sklearn/linear_model/_glm/glm.py
View file
Open in desktop
7E5A
Original file line numberDiff line number Diff line change
Expand Up @@ -430,6 +430,23 @@ class PoissonRegressor(GeneralizedLinearRegressor):

n_iter_ : int
Actual number of iterations used in the solver.

Examples
----------
>>> from sklearn import linear_model
>>> clf = linear_model.PoissonRegressor()
>>> X = [[1, 2], [2, 3], [3, 4], [4, 3]]
>>> y = [12, 17, 22, 21]
>>> clf.fit(X, y)
PoissonRegressor()
>>> clf.score(X, y)
0.990...
>>> clf.coef_
array([0.121..., 0.158...])
>>> clf.intercept_
2.088...
>>> clf.predict([[1, 1], [3, 4]])
array([10.676..., 21.875...])
"""
def __init__(self, *, alpha=1.0, fit_intercept=True, max_iter=100,
tol=1e-4, warm_start=False, verbose=0):
Expand Down
0