import matplotlib.pyplot as plt

plt.ylabel("Accuracy")
plt.ylim(0.9,1.0) # Define the interval of y-axis
plt.xlabel("Disambiguation Confidence")
x_value =[0.2,0.3,0.4,0.5,0.7] 

KNN_Acc =[0.994,0.992,0.980,0.982,0.965]
RF_Acc  =[0.994,0.994,0.986,0.982,0.943]
Naive_B =[0.959,0.960,0.973,0.936,0.933]
plt.plot(x_value, KNN_Acc, marker='o', color='r', label='KNN')#KNN
plt.plot(x_value, RF_Acc, marker='^', color='g', label='Random Forest') #RF
plt.plot(x_value, Naive_B, marker='*', color='b', label='Naive_Bayes')#NBB
plt.legend(loc='upper right')
plt.show()

25a8385cbcc54b7845c7b24bad7dccf5014c1303
<mask token>

<mask token>
plt.ylabel('Accuracy')
plt.ylim(0.9, 1.0)
plt.xlabel('Disambiguation Confidence')
<mask token>
plt.plot(x_value, KNN_Acc, marker='o', color='r', label='KNN')
plt.plot(x_value, RF_Acc, marker='^', color='g', label='Random Forest')
plt.plot(x_value, Naive_B, marker='*', color='b', label='Naive_Bayes')
plt.legend(loc='upper right')
plt.show()

<mask token>
plt.ylabel('Accuracy')
plt.ylim(0.9, 1.0)
plt.xlabel('Disambiguation Confidence')
x_value = [0.2, 0.3, 0.4, 0.5, 0.7]
KNN_Acc = [0.994, 0.992, 0.98, 0.982, 0.965]
RF_Acc = [0.994, 0.994, 0.986, 0.982, 0.943]
Naive_B = [0.959, 0.96, 0.973, 0.936, 0.933]
plt.plot(x_value, KNN_Acc, marker='o', color='r', label='KNN')
plt.plot(x_value, RF_Acc, marker='^', color='g', label='Random Forest')
plt.plot(x_value, Naive_B, marker='*', color='b', label='Naive_Bayes')
plt.legend(loc='upper right')
plt.show()

import matplotlib.pyplot as plt
plt.ylabel('Accuracy')
plt.ylim(0.9, 1.0)
plt.xlabel('Disambiguation Confidence')
x_value = [0.2, 0.3, 0.4, 0.5, 0.7]
KNN_Acc = [0.994, 0.992, 0.98, 0.982, 0.965]
RF_Acc = [0.994, 0.994, 0.986, 0.982, 0.943]
Naive_B = [0.959, 0.96, 0.973, 0.936, 0.933]
plt.plot(x_value, KNN_Acc, marker='o', color='r', label='KNN')
plt.plot(x_value, RF_Acc, marker='^', color='g', label='Random Forest')
plt.plot(x_value, Naive_B, marker='*', color='b', label='Naive_Bayes')
plt.legend(loc='upper right')
plt.show()

import matplotlib.pyplot as plt

plt.ylabel("Accuracy")
plt.ylim(0.9,1.0) # Define the interval of y-axis
plt.xlabel("Disambiguation Confidence")
x_value =[0.2,0.3,0.4,0.5,0.7] 

KNN_Acc =[0.994,0.992,0.980,0.982,0.965]
RF_Acc  =[0.994,0.994,0.986,0.982,0.943]
Naive_B =[0.959,0.960,0.973,0.936,0.933]
plt.plot(x_value, KNN_Acc, marker='o', color='r', label='KNN')#KNN
plt.plot(x_value, RF_Acc, marker='^', color='g', label='Random Forest') #RF
plt.plot(x_value, Naive_B, marker='*', color='b', label='Naive_Bayes')#NBB
plt.legend(loc='upper right')
plt.show()
