classfication best random_state
#x->independent variables
#y->dependent variable
#model->algorithm
from sklearn.model_selection import train_test_split
from sklearn.metrics import roc_auc_score,recall_score
from sklearn.linear_model import LogisticRegression
def maxaccuracy_score(model,x,y):
max_accuracy=0
for r_state in range(42,101):
x_train,x_test,y_train,y_test=train_test_split(x,y,random_state=r_state,stratify=y)
model.fit(x_train,y_train)
pred=model.predict(x_test)
score=accuracy_score(y_test,pred)
roc_score=roc_auc_score(y_test,pred)
if score>max_accuracy:
max_accuracy=score
final_r_state=r_state
print('max_accuracy_score is at random_state ',final_r_state,' which is ',max_accuracy,'and roc_auc_score=',roc_score)
return final_r_state