解决FutureWarning: Default solver will be changed to 'lbfgs' in 0.22问题

代码：

from sklearn import model_selection
from sklearn.linear_model import LogisticRegression
# Initialize our algorithm
alg = LogisticRegression(random_state=1)
# Change to alg = LogisticRegression(random_state=1, solver='liblinear')
# Compute the accuracy score for all the cross validation folds.  (much simpler than what we did before!)
scores = model_selection.cross_val_score(alg, titanic[predictors], titanic["Survived"], cv=3)
# Take the mean of the scores (because we have one for each fold)
print(scores.mean())

警告信息：
FutureWarning: Default solver will be changed to ‘lbfgs’ in 0.22. Specify a solver to silence this warning. FutureWarning)

警告分析：

sklearn.linear_model.LogisticRegression中在新的0.22版本中将“liblinear”变为“lbfgs”，所以需要指定slover的参数值。官方文档内容如下：

solver : str, {‘newton-cg’, ‘lbfgs’, ‘liblinear’, ‘sag’, ‘saga’}, optional (default=’liblinear’).
Algorithm to use in the optimization problem.

For small datasets, ‘liblinear’ is a good choice, whereas ‘sag’ and ‘saga’ are faster for large ones.
For multiclass problems, only ‘newton-cg’, ‘sag’, ‘saga’ and ‘lbfgs’ handle multinomial loss; ‘liblinear’ is limited to one-versus-rest schemes.
‘newton-cg’, ‘lbfgs’, ‘sag’ and ‘saga’ handle L2 or no penalty
‘liblinear’ and ‘saga’ also handle L1 penalty
‘saga’ also supports ‘elasticnet’ penalty
‘liblinear’ does not handle no penalty
Note that ‘sag’ and ‘saga’ fast convergence is only guaranteed on features with approximately the same scale. You can preprocess the data with a scaler from sklearn.preprocessing.

New in version 0.17: Stochastic Average Gradient descent solver.

New in version 0.19: SAGA solver.

Changed in version 0.20: Default will change from ‘liblinear’ to ‘lbfgs’ in 0.22.

参考链接：SKlearn官方文档