avoid randon state in python model code example

Example 1: random_state

Here you pass an integer, which will act as the seed for the random number generator during the split. Or, you can also pass an instance of the RandomState class, which will become the number generator. If you don’t pass anything, the RandomState instance used by np.random will be used instead.

Example 2: regression best random_state

#x->independent variable
#y->dependent variable
#model->algorithm
def maxr2_score(model,x,y):
    max_r_score=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)
        model.fit(x_train,y_train)
        pred=model.predict(x_test)
        score=r2_score(y_test,pred)
        
        if score>max_r_score:
            max_r_score=score
            final_r_state=r_state
    print('max_r2_score is at random_state  ',final_r_state,'  which is  ',max_r_score)
    return final_r_state

Example 3: what is random state

The random state is simply the lot number of the set generated randomly in any operation. We can specify this lot number whenever we want the same set again.