How come sklearn LogisticRegressionCV has worse performance than LogisticRegression?

Question

I am using Anaconda jupyter notebook to do multinomial logistic regression. The output has 3 possible results {1, 2, 3}. There are 32 independent variables. I have 50k records and I did a 80-20 split.

I am pretty new on this.

My questions are:

1. How come logistic model all give prediction of 1? basically the same as baseline model?
2. How LogisticRegressionCV with class_weight = 'balanced' has such a low accuracy? while the one without class_weight has a 'high' accuracy?

What did I do wrong?

baseline model

from sklearn.dummy import DummyClassifier
dummy_model = DummyClassifier(strategy = 'most_frequent', random_state = 0)
dummy_model.fit(x_train, y_train)

result:

accuracy score: 0.9706794756329431

confusion matrix:

[[9700    0    0]

 [ 211    0    0]

 [  82    0    0]]

classification report:

         precision    recall  f1-score   support

      1       0.97      1.00      0.99      9700
      2       0.00      0.00      0.00       211
      3       0.00      0.00      0.00        82

avg / total       0.94      0.97      0.96      9993

logit regression

from sklearn.linear_model import LogisticRegression
logit_model = LogisticRegression(C=0.05, random_state=18, class_weight='balanced', penalty='l1')
logit_model.fit(x_train, y_train)

result: accuracy score: 0.9706794756329431

confusion matrix:

[[9700    0    0]

 [ 211    0    0]

 [  82    0    0]]

classification report:

         precision    recall  f1-score   support

      1       0.97      1.00      0.99      9700
      2       0.00      0.00      0.00       211
      3       0.00      0.00      0.00        82

avg / total       0.94      0.97      0.96      9993

logit regression by GridSearchCV:

logit_model_base = LogisticRegression(random_state = 18)
from sklearn.model_selection import GridSearchCV
parameters = {'C': [0.03, 0.05, 0.08, 0.1, 0.3, 0.5, 10], 'penalty': ['l1', 'l2']}
logit_model_best = GridSearchCV(logit_model_base, param_grid = parameters, cv = 3) 
logit_model_best.fit(x_train, y_train)

result:

accuracy score: 0.9706794756329431

confusion matrix:

[[9700    0    0]

 [ 211    0    0]

 [  82    0    0]]

classification report:

         precision    recall  f1-score   support

      1       0.97      1.00      0.99      9700
      2       0.00      0.00      0.00       211
      3       0.00      0.00      0.00        82

avg / total       0.94      0.97      0.96      9993

LogisticRegressionCV with class_weight = 'balanced'

from sklearn.linear_model import LogisticRegressionCV
logit_model_cv = LogisticRegressionCV(cv = 10, class_weight = 'balanced')
logit_model_cv.fit(x_train, y_train)

result:

accuracy score: 0.2982087461222856

confusion matrix:

[[2831 3384 3485]

 [  36  104   71]

 [   9   28   45]]

classification report:

         precision    recall  f1-score   support

      1       0.98      0.29      0.45      9700
      2       0.03      0.49      0.06       211
      3       0.01      0.55      0.02        82

avg / total       0.96      0.30      0.44      9993

LogisticRegressionCV without class_weight = 'balanced'

from sklearn.linear_model import LogisticRegressionCV
logit_model_cv = LogisticRegressionCV(cv = 10)
logit_model_cv.fit(x_train, y_train)

result:

accuracy score: 0.9706794756329431

confusion matrix:

[[9700    0    0]

 [ 211    0    0]

 [  82    0    0]]

classification report:

         precision    recall  f1-score   support

      1       0.97      1.00      0.99      9700
      2       0.00      0.00      0.00       211
      3       0.00      0.00      0.00        82

avg / total       0.94      0.97      0.96      9993