thomasjpfan on main
DOC Fix typo in common_pitfalls… (compare)
help wanted. I suppose a new one can pick this and conclude the PR also taking into account the comments of the reviewers. What's the best here? Create a new PR that refers to the already existing one?
I have two files that contain Event Name, Event City, Event Venue, Event State but in both files it's written in different ways or you can assume both the files are from different source.
I want to create a Machine learning-based algorithm that can do the matching.
I have tried with fuzzy-wuzzy to get string similarity.
Can anyone please tell me if I want to solve this with Deep Learning what would be the approach. Thanks @amueller
Dear, I i tried to tune hyperparameters of scikit GradientBoostingRegressor model using the Hyperopt optimizer. I set search space for learning_rate parameter in the range [0.01, 1] by many ways (for example : ""'learning_rate': hp.quniform('learning_rate', 0.01, 1, 0.05)"" or as simple array ""[0.01, 0.02, 0.03, 0.1]"") but when I run the code hyperopt start to calculation and I get the error " ValueError: learning_rate must be greater than 0 but was 0".
I do not know what is problem in the code because zero value is not in the parameter's scope. How zero value come to function?
Please help me to solve this problem.
hp.loguniform(-3, 0)or someting similar.
System: python: 3.7.4 (default, Oct 4 2019, 06:57:26) [GCC 9.2.0] executable: /home/nico/.virtualenvs/sklearn/bin/python machine: Linux-5.3.1-arch1-1-ARCH-x86_64-with-arch Python dependencies: pip: 19.0.3 setuptools: 40.8.0 sklearn: 0.23.dev0 numpy: 1.17.1 scipy: 1.3.0 Cython: 0.29.10 pandas: 0.24.2 matplotlib: 3.0.0 joblib: 0.13.2 Built with OpenMP: True
lib/hmmlearn/tests/test_gmm_hmm_new.py::TestGMMHMMWithSphericalCovars::test_fit_zero_variance lib/hmmlearn/tests/test_gmm_hmm_new.py::TestGMMHMMWithTiedCovars::test_fit_sparse_data /home/nico/dev/hmmlearn/lib/hmmlearn/hmm.py:849: RuntimeWarning: underflow encountered in multiply post_comp_mix = post_comp[:, :, np.newaxis] * post_mix lib/hmmlearn/tests/test_gmm_hmm_new.py::TestGMMHMMWithDiagCovars::test_fit_zero_variance /home/nico/dev/hmmlearn/lib/hmmlearn/stats.py:47: RuntimeWarning: divide by zero encountered in log + np.dot(X ** 2, (1.0 / covars).T)) lib/hmmlearn/tests/test_gmm_hmm_new.py::TestGMMHMMWithDiagCovars::test_fit_zero_variance /home/nico/dev/hmmlearn/lib/hmmlearn/stats.py:47: RuntimeWarning: divide by zero encountered in true_divide + np.dot(X ** 2, (1.0 / covars).T)) lib/hmmlearn/tests/test_gmm_hmm_new.py::TestGMMHMMWithDiagCovars::test_fit_zero_variance /home/nico/dev/hmmlearn/lib/hmmlearn/stats.py:47: RuntimeWarning: invalid value encountered in add + np.dot(X ** 2, (1.0 / covars).T)) -- Docs: https://docs.pytest.org/en/latest/warnings.html Results (20.38s): 93 passed 3 xpassed 15 xfailed
Hi, I want to apply Multinomial Logistic Regression to compute winning probabilities for each contestant in my races.
The Data I want to feed in my model look like the image above.
I'm tring to understand how should I feed the target class to my model because every race can have a different number of runners, the target class for race A has 5 contestants, instead target class for race B has just 4 contestants.
Is there a way to model this using scikit-learn?