How to correct the programming for KNN

I get the answer but the output pictures are wrong - may I know which part on my programming is wrong

# read in the iris data

from sklearn.datasets import load_iris

iris = load_iris()

# create X (features) and y (response)

X = iris.data

y = iris.target



from sklearn.neighbors import KNeighborsClassifier

k1 = (1, 2, 3, 4, 5, 6, 7, 8, 9)

k2 = (10, 15, 20, 25, 30, 35, 40)

knn = KNeighborsClassifier(n_neighbors=10)

knn.fit(X, y)

y_pred = knn.predict(X)



from sklearn import metrics

metrics.accuracy_score(y,y_pred)

knn = KNeighborsClassifier(n_neighbors=1)

from sklearn.cross_validation import train_test_split

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=0)



# import Matplotlib (scientific plotting library)

import matplotlib.pyplot as plt

import numpy as np

# try K=1 through K=9 and record testing accuracy

k1_range = range(1, 9)

k2_range = range(10, 40)

# create Python dictionary using  

scores1 = 

for k1 in k1_range:

         knn = KNeighborsClassifier(n_neighbors=k1, metric='minkowski', p=2)

         knn.fit(X_train, y_train)

         y_pred = knn.predict(X_test)

         scores1.append(metrics.accuracy_score(y_test, y_pred))



scores2 =          

for k2 in k2_range:

         knn = KNeighborsClassifier(n_neighbors=k2, metric='minkowski', p=2)

         knn.fit(X_train, y_train)

         y_pred = knn.predict(X_test)

         scores2.append(metrics.accuracy_score(y_test, y_pred))         



# plot the relationship between K and testing accuracy

# plt.plot(x_axis, y_axis)

plt.subplot(211)         

plt.plot(k1_range, scores1)

plt.yticks(np.arange(0.93, 0.98, 0.03))

plt.xlabel('Number of neighbors')

plt.ylabel('Accuracy')



plt.subplot(212)

plt.plot(k2_range, scores2)

plt.yticks(np.arange(0.91, 0.98, 0.03))

plt.xlabel('Number of neighbors')

plt.ylabel('Accuracy')



plt.tight_layout() 

plt.show()

Please see the attached file -

enter image description here

Please help me to correct the pictures as refer to the attached image file

asked 7 mins ago

vokoyov

New contributor

add a comment |

I get the answer but the output pictures are wrong - may I know which part on my programming is wrong

# read in the iris data

from sklearn.datasets import load_iris

iris = load_iris()

# create X (features) and y (response)

X = iris.data

y = iris.target



from sklearn.neighbors import KNeighborsClassifier

k1 = (1, 2, 3, 4, 5, 6, 7, 8, 9)

k2 = (10, 15, 20, 25, 30, 35, 40)

knn = KNeighborsClassifier(n_neighbors=10)

knn.fit(X, y)

y_pred = knn.predict(X)



from sklearn import metrics

metrics.accuracy_score(y,y_pred)

knn = KNeighborsClassifier(n_neighbors=1)

from sklearn.cross_validation import train_test_split

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=0)



# import Matplotlib (scientific plotting library)

import matplotlib.pyplot as plt

import numpy as np

# try K=1 through K=9 and record testing accuracy

k1_range = range(1, 9)

k2_range = range(10, 40)

# create Python dictionary using  

scores1 = 

for k1 in k1_range:

         knn = KNeighborsClassifier(n_neighbors=k1, metric='minkowski', p=2)

         knn.fit(X_train, y_train)

         y_pred = knn.predict(X_test)

         scores1.append(metrics.accuracy_score(y_test, y_pred))



scores2 =          

for k2 in k2_range:

         knn = KNeighborsClassifier(n_neighbors=k2, metric='minkowski', p=2)

         knn.fit(X_train, y_train)

         y_pred = knn.predict(X_test)

         scores2.append(metrics.accuracy_score(y_test, y_pred))         



# plot the relationship between K and testing accuracy

# plt.plot(x_axis, y_axis)

plt.subplot(211)         

plt.plot(k1_range, scores1)

plt.yticks(np.arange(0.93, 0.98, 0.03))

plt.xlabel('Number of neighbors')

plt.ylabel('Accuracy')



plt.subplot(212)

plt.plot(k2_range, scores2)

plt.yticks(np.arange(0.91, 0.98, 0.03))

plt.xlabel('Number of neighbors')

plt.ylabel('Accuracy')



plt.tight_layout() 

plt.show()

Please see the attached file -

enter image description here

Please help me to correct the pictures as refer to the attached image file

asked 7 mins ago

vokoyov

New contributor

add a comment |

I get the answer but the output pictures are wrong - may I know which part on my programming is wrong

# read in the iris data

from sklearn.datasets import load_iris

iris = load_iris()

# create X (features) and y (response)

X = iris.data

y = iris.target



from sklearn.neighbors import KNeighborsClassifier

k1 = (1, 2, 3, 4, 5, 6, 7, 8, 9)

k2 = (10, 15, 20, 25, 30, 35, 40)

knn = KNeighborsClassifier(n_neighbors=10)

knn.fit(X, y)

y_pred = knn.predict(X)



from sklearn import metrics

metrics.accuracy_score(y,y_pred)

knn = KNeighborsClassifier(n_neighbors=1)

from sklearn.cross_validation import train_test_split

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=0)



# import Matplotlib (scientific plotting library)

import matplotlib.pyplot as plt

import numpy as np

# try K=1 through K=9 and record testing accuracy

k1_range = range(1, 9)

k2_range = range(10, 40)

# create Python dictionary using  

scores1 = 

for k1 in k1_range:

         knn = KNeighborsClassifier(n_neighbors=k1, metric='minkowski', p=2)

         knn.fit(X_train, y_train)

         y_pred = knn.predict(X_test)

         scores1.append(metrics.accuracy_score(y_test, y_pred))



scores2 =          

for k2 in k2_range:

         knn = KNeighborsClassifier(n_neighbors=k2, metric='minkowski', p=2)

         knn.fit(X_train, y_train)

         y_pred = knn.predict(X_test)

         scores2.append(metrics.accuracy_score(y_test, y_pred))         



# plot the relationship between K and testing accuracy

# plt.plot(x_axis, y_axis)

plt.subplot(211)         

plt.plot(k1_range, scores1)

plt.yticks(np.arange(0.93, 0.98, 0.03))

plt.xlabel('Number of neighbors')

plt.ylabel('Accuracy')



plt.subplot(212)

plt.plot(k2_range, scores2)

plt.yticks(np.arange(0.91, 0.98, 0.03))

plt.xlabel('Number of neighbors')

plt.ylabel('Accuracy')



plt.tight_layout() 

plt.show()

Please see the attached file -

enter image description here

Please help me to correct the pictures as refer to the attached image file

asked 7 mins ago

vokoyov

New contributor

I get the answer but the output pictures are wrong - may I know which part on my programming is wrong

# read in the iris data

from sklearn.datasets import load_iris

iris = load_iris()

# create X (features) and y (response)

X = iris.data

y = iris.target



from sklearn.neighbors import KNeighborsClassifier

k1 = (1, 2, 3, 4, 5, 6, 7, 8, 9)

k2 = (10, 15, 20, 25, 30, 35, 40)

knn = KNeighborsClassifier(n_neighbors=10)

knn.fit(X, y)

y_pred = knn.predict(X)



from sklearn import metrics

metrics.accuracy_score(y,y_pred)

knn = KNeighborsClassifier(n_neighbors=1)

from sklearn.cross_validation import train_test_split

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=0)



# import Matplotlib (scientific plotting library)

import matplotlib.pyplot as plt

import numpy as np

# try K=1 through K=9 and record testing accuracy

k1_range = range(1, 9)

k2_range = range(10, 40)

# create Python dictionary using  

scores1 = 

for k1 in k1_range:

         knn = KNeighborsClassifier(n_neighbors=k1, metric='minkowski', p=2)

         knn.fit(X_train, y_train)

         y_pred = knn.predict(X_test)

         scores1.append(metrics.accuracy_score(y_test, y_pred))



scores2 =          

for k2 in k2_range:

         knn = KNeighborsClassifier(n_neighbors=k2, metric='minkowski', p=2)

         knn.fit(X_train, y_train)

         y_pred = knn.predict(X_test)

         scores2.append(metrics.accuracy_score(y_test, y_pred))         



# plot the relationship between K and testing accuracy

# plt.plot(x_axis, y_axis)

plt.subplot(211)         

plt.plot(k1_range, scores1)

plt.yticks(np.arange(0.93, 0.98, 0.03))

plt.xlabel('Number of neighbors')

plt.ylabel('Accuracy')



plt.subplot(212)

plt.plot(k2_range, scores2)

plt.yticks(np.arange(0.91, 0.98, 0.03))

plt.xlabel('Number of neighbors')

plt.ylabel('Accuracy')



plt.tight_layout() 

plt.show()

Please see the attached file -

enter image description here

Please help me to correct the pictures as refer to the attached image file

python k-nn ai

asked 7 mins ago

vokoyov

New contributor

asked 7 mins ago

vokoyov

New contributor

asked 7 mins ago

vokoyov

New contributor

asked 7 mins ago

vokoyov

asked 7 mins ago

vokoyov

New contributor

vokoyov is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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