Sklearn Random Feature Importances Identical for Predicting Different Response Variables

I have created four random forest models they have the same X data, but their y data are four different response variables. The sklearn random forest feature importance is identical for all four. All four models achieve their purpose and make different predictions, but their random forest feature importance is the same.

Has anyone experienced this before?

I created the models with a series of nested objects like illustrated below. I used the same code before without having identical random forest feature importances, there was however the difference that inside each object I ran a 3-fold CV to determine max_features, whereas here I just used the default which is all of them.

Current code:

class NoCVMethod:
    def __init__(self, X_train, y_train, X_test, y_test, y, Method):
        self.clf = Method
        self.clf.fit(X_train, y_train)
        self.predictions = self.clf.predict(X_test)
        self.rev_preds = rev_pred(y[-(13978+97):].values,self.predictions)
        self.residuals = y_test - self.rev_preds
        self.RMSE = np.mean((self.residuals)**2)**0.5

class Different_variables:
    def __init__(self, X_train, y_train, X_test, y_test, Method):
        self.TSS = NoCVMethod(X_train, y_train[y_train.columns.tolist()[0]], X_test, y_test[y_test.columns.tolist()[0]], y[y.columns.tolist()[0]], Method)
        self.NOx = NoCVMethod(X_train, y_train[y_train.columns.tolist()[1]], X_test, y_test[y_test.columns.tolist()[1]], y[y.columns.tolist()[1]], Method)
        self.NH4 = NoCVMethod(X_train, y_train[y_train.columns.tolist()[2]], X_test, y_test[y_test.columns.tolist()[2]], y[y.columns.tolist()[2]], Method)
        self.PO4 = NoCVMethod(X_train, y_train[y_train.columns.tolist()[3]], X_test, y_test[y_test.columns.tolist()[3]], y[y.columns.tolist()[3]], Method)

Old code:

class CVMethod:
    def __init__(self, X_train, y_train, X_test, y_test, y, param_dict, Method):
        self.pipeline = Pipeline([
            ('scale', StandardScaler()),
            ('clf', Method)
        ]) 
        self.param_grid = param_dict
        self.grid = GridSearchCV(self.pipeline, param_grid = self.param_grid, cv = 3, verbose = False, n_jobs = -1)
        self.grid.fit(X_train, y_train)
        self.predictions = self.grid.predict(X_test).ravel()
        self.rev_preds = rev_pred(y[-(13978+97):].values,self.predictions)
        self.residuals = y_test - self.rev_preds
        self.RMSE = np.mean((self.residuals)**2)**0.5

class CVDifferent_variables:
    def __init__(self, X_train, y_train, X_test, y_test, param_dict, Method):
        self.TSS = CVMethod(X_train, y_train[y_train.columns.tolist()[0]], X_test, y_test[y_test.columns.tolist()[0]], y[y.columns.tolist()[0]], param_dict, Method)
        self.NOx = CVMethod(X_train, y_train[y_train.columns.tolist()[1]], X_test, y_test[y_test.columns.tolist()[1]], y[y.columns.tolist()[1]], param_dict, Method)
        self.NH4 = CVMethod(X_train, y_train[y_train.columns.tolist()[2]], X_test, y_test[y_test.columns.tolist()[2]], y[y.columns.tolist()[2]], param_dict, Method)
        self.PO4 = CVMethod(X_train, y_train[y_train.columns.tolist()[3]], X_test, y_test[y_test.columns.tolist()[3]], y[y.columns.tolist()[3]], param_dict, Method)
``

from sklearn.base import clone

class NoCVMethod:
    [...]
    self.clf = clone(Method) # only copy the estimator
    # OR
    self.clf = deepcopy(Method) # if you want to also copy the data estimator