LR Grid Search

from sklearn.model_selection import GridSearchCV, StratifiedKFold
lr = LogisticRegression(solver='newton-cg')

#Setting the range for class weights
weights = np.linspace(0.0,0.99,200)

#Creating a dictionary grid for grid search
param_grid = {'class_weight': [{0:x, 1:1.0-x} for x in weights]}

#Fitting grid search to the train data with 5 folds
gridsearch = GridSearchCV(estimator= lr, 
                          param_grid= param_grid,
                          cv=StratifiedKFold(), 
                          n_jobs=-1, 
                          scoring='f1', 
                          verbose=2).fit(x_train, y_train)

#Ploting the score for different values of weight
sns.set_style('whitegrid')
plt.figure(figsize=(12,8))
weigh_data = pd.DataFrame({ 'score': gridsearch.cv_results_['mean_test_score'], 'weight': (1- weights)})
sns.lineplot(weigh_data['weight'], weigh_data['score'])
plt.xlabel('Weight for class 1')
plt.ylabel('F1 score')
plt.xticks([round(i/10,1) for i in range(0,11,1)])
plt.title('Scoring for different class weights', fontsize=24)