Gfyuki

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