I'm currently taking udemy's "Machine Learning with python: An Introduction to Identification Learned with scikit-learn". Since sample code is distributed for each theme in this course, I am grateful that I do not have to write it, but a warning message appears.
jupyter-lab:1.2.6 python:3.7.7 scikit-learn:0.22.1
from sklearn import linear_model
clf = linear_model.LogisticRegression()
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import LeaveOneOut
loocv = LeaveOneOut()
scores = cross_val_score(clf, X, y,cv=loocv)
When I ran
ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
And warning. Moreover, many times on the scale of thousands of lines.
Follow the warning and use the same warning text as Logistic Regression (max_iter = 1000 ^ 4000).
When I checked it, it was said that it would repeat until it converged with max_iter = -1, so when I passed -1 as an argument and executed it,
max_iter gets angry at a positive number.
If there is no choice but to set max_iter = 1000 ^ 5000, the process will not end.
When someone else does it, it takes less than a second to learn, so maybe something is wrong with my environment, but I don't know.
In the first place, it is not an error statement but a warning statement, so it may be learning repeatedly up to the specified number of times, but it would be a problem if thousands of lines of warning statements were issued each time. In addition, X and y are each less than 1MB in size, and I tried it with google's colab, but the same result was obtained.
If anyone has any idea, I would appreciate it if you could tell me.
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