FutureWarning: Setting a random_state has no effect since shuffle is False

Делаю "Hello world" в машинном обучении, используя набор данных Iris. У меня уже есть приемлемый результат для ввода этой модели, я использую 80% информации для ее обучения, а остальные 20%-для проверки. Я использую 6 алгоритмов прогнозирования.

    # Load libraries    
    import pandas
    from pandas.plotting import scatter_matrix
    import matplotlib.pyplot as plt
    from sklearn import model_selection
    from sklearn.metrics import classification_report
    from sklearn.metrics import confusion_matrix
    from sklearn.metrics import accuracy_score
    from sklearn.linear_model import LogisticRegression
    from sklearn.tree import DecisionTreeClassifier
    from sklearn.neighbors import KNeighborsClassifier
    from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
    from sklearn.naive_bayes import GaussianNB
    from sklearn.svm import SVC
    
    # Load dataset
    url = "https://raw.githubusercontent.com/jbrownlee/Datasets/master/iris.csv"
    names = ['sepal-length', 'sepal-width', 'petal-length', 'petal-width', 'class']
    dataset = pandas.read_csv(url, names=names)
    
    # Split-out validation dataset
    array = dataset.values
    X = array[:,0:4]
    Y = array[:,4]
    validation_size = 0.20
    seed = 7
    X_train, X_validation, Y_train, Y_validation = model_selection.train_test_split(X, Y, test_size=validation_size, random_state=seed)
    
    # Test options and evaluation metric
    seed = 7
    scoring = 'accuracy'
    
    # Spot Check Algorithms
    models = []
    models.append(('LR', LogisticRegression()))
    models.append(('LDA', LinearDiscriminantAnalysis()))
    models.append(('KNN', KNeighborsClassifier()))
    models.append(('CART', DecisionTreeClassifier()))
    models.append(('NB', GaussianNB()))
    models.append(('SVM', SVC()))
    # evaluate each model in turn
    results = []
    names = []
    for name, model in models:
        kfold = model_selection.KFold(n_splits=10, random_state=seed)
        cv_results = model_selection.cross_val_score(model, X_train, Y_train, cv=kfold, scoring=scoring)
        results.append(cv_results)
        names.append(name)
        msg = "%s: %f (%f)" % (name, cv_results.mean(), cv_results.std())
        print(msg)

После этого я получаю следующее:

C:\Users\Dexp\anaconda3\lib\site-packages\sklearn\model_selection\_split.py:293: FutureWarning: Setting a random_state has no effect since shuffle is False. This will raise an error in 0.24. You should leave random_state to its default (None), or set shuffle=True.
  warnings.warn(
C:\Users\Dexp\anaconda3\lib\site-packages\sklearn\linear_model\_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Dexp\anaconda3\lib\site-packages\sklearn\linear_model\_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Dexp\anaconda3\lib\site-packages\sklearn\linear_model\_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
LR: 0.983333 (0.033333)
LDA: 0.975000 (0.038188)
C:\Users\Dexp\anaconda3\lib\site-packages\sklearn\model_selection\_split.py:293: FutureWarning: Setting a random_state has no effect since shuffle is False. This will raise an error in 0.24. You should leave random_state to its default (None), or set shuffle=True.
  warnings.warn(
C:\Users\Dexp\anaconda3\lib\site-packages\sklearn\model_selection\_split.py:293: FutureWarning: Setting a random_state has no effect since shuffle is False. This will raise an error in 0.24. You should leave random_state to its default (None), or set shuffle=True.
  warnings.warn(
KNN: 0.983333 (0.033333)
CART: 0.975000 (0.038188)
NB: 0.975000 (0.053359)
C:\Users\Dexp\anaconda3\lib\site-packages\sklearn\model_selection\_split.py:293: FutureWarning: Setting a random_state has no effect since shuffle is False. This will raise an error in 0.24. You should leave random_state to its default (None), or set shuffle=True.
  warnings.warn(
C:\Users\Dexp\anaconda3\lib\site-packages\sklearn\model_selection\_split.py:293: FutureWarning: Setting a random_state has no effect since shuffle is False. This will raise an error in 0.24. You should leave random_state to its default (None), or set shuffle=True.
  warnings.warn(
C:\Users\Dexp\anaconda3\lib\site-packages\sklearn\model_selection\_split.py:293: FutureWarning: Setting a random_state has no effect since shuffle is False. This will raise an error in 0.24. You should leave random_state to its default (None), or set shuffle=True.
  warnings.warn(
    SVM: 0.991667 (0.025000)

Это можно как-то исправить??