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May I know how to modify my Python programming thus it will be get the same result as refer to the attached image file?

import numpy as np
from sklearn import datasets as data
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
import matplotlib.pyplot as plt
def load_data(feature_len = 4):
 return data.load_iris()['data'][:, :feature_len], data.load_iris()['target']
def tr_te_split(data, target, test_ratio = .2):
 return train_test_split(data, target, test_size=test_ratio, random_state=0)
def build_model():
 return DecisionTreeClassifier(criterion='entropy'), RandomForestClassifier(criterion='entropy', n_estimators=10, random_state=1)
def train(model, x, y):
 model.fit(x, y)
 print(" Training Accuracy = ".format(str(model.__class__).split('.')[-1].split("'")[0], model.score(x, y)))
 return model
def evaluate(model, x, y):
 print(" Testing Accuracy = n".format(str(model.__class__).split('.')[-1].split("'")[0], model.score(x, y)))
 return accuracy_score(model.predict(x), y)
def main():
 data, target = load_data()

 tree_score = []
 rf_score = []
 for feature_len in range(1, data.shape[-1] + 1):
 print("Use features.".format(feature_len))
 data, target = load_data(feature_len)
 x_tr, x_te, y_tr, y_te = tr_te_split(data, target, .4)

 tree_model, rf_model = build_model()

 tree_model_trained = train(tree_model, x_tr, y_tr)
 tree_score.append(evaluate(tree_model_trained, x_te, y_te))

 rf_model_trained = train(rf_model, x_tr, y_tr)
 rf_score.append(evaluate(rf_model_trained, x_te, y_te))

 # draw
 tree_plt = plt
 tree_plt.plot(tree_score)
 tree_plt.xlabel('Number of features')
 tree_plt.xticks([0,1,2,3],('1','2','3','4'))
 tree_plt.ylabel('Accuracy')
 tree_plt.title('Decision Tree')
 tree_plt.show()

 rf_plt = plt
 rf_plt.plot(rf_score)
 rf_plt.xlabel('Number of features')
 rf_plt.xticks([0,1,2,3],('1','2','3','4'))
 rf_plt.ylabel('Accuracy')
 plt.yticks(np.arange(0.90, 0.95, 0.01))
 rf_plt.title('Ransom Forest')
 rf_plt.show()

if __name__ == '__main__':
 main() 

Please see the image file -

Please help me on this case