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python implementation of k-nearest neighbor regression with equal and unequal weights

python implementation of k-nearest neighbor regression with equal and unequal weights

Updated January 23, 2019 13:54:13 by UESTC_C2_403
Today, I'd like to share with you a python implementation of K nearest neighbor regression, using equal and unequal weights, with good reference value, I hope it will help you. Together follow the editor over to see it

As shown below:

from  import load_boston
 
boston = load_boston()
 
from sklearn.cross_validation import train_test_split
 
import numpy as np;
 
X = 
y = 
 
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state = 33, test_size = 0.25)
 
print 'The max target value is: ', ()
print 'The min target value is: ', ()
print 'The average terget value is: ', ()
 
from  import StandardScaler
 
ss_X = StandardScaler()
ss_y = StandardScaler()
 
X_train = ss_X.fit_transform(X_train)
X_test = ss_X.transform(X_test)
y_train = ss_y.fit_transform(y_train)
y_test = ss_y.transform(y_test)
 
from  import KNeighborsRegressor
 
uni_knr = KNeighborsRegressor(weights = 'uniform')
uni_knr.fit(X_train, y_train)
uni_knr_y_predict = uni_knr.predict(X_test)
 
dis_knr = KNeighborsRegressor(weights = 'distance')
dis_knr.fit(X_train, y_train)
dis_knr_y_predict = dis_knr.predict(X_test)
 
from  import r2_score, mean_squared_error, mean_absolute_error
 
print 'R-squared value of uniform weights KNeighorRegressor is: ', uni_knr.score(X_test, y_test)
print 'The mean squared error of uniform weights KNeighorRegressor is: ', mean_squared_error(ss_y.inverse_transform(y_test), ss_y.inverse_transform(uni_knr_y_predict))
print 'The mean absolute error of uniform weights KNeighorRegressor is: ', mean_absolute_error(ss_y.inverse_transform(y_test), ss_y.inverse_transform(uni_knr_y_predict))
 
print 'R-squared of distance weights KNeighorRegressor is: ', dis_knr.score(X_test, y_test)
print 'the value of mean squared error of distance weights KNeighorRegressor is: ', mean_squared_error(ss_y.inverse_transform(y_test), ss_y.inverse_transform(dis_knr_y_predict))
print 'the value of mean ssbsolute error of distance weights KNeighorRegressor is: ', mean_absolute_error(ss_y.inverse_transform(y_test), ss_y.inverse_transform(dis_knr_y_predict))

The above this python implementation of K nearest neighbor regression, using equal and unequal weights is all I have to share with you, I hope to give you a reference, and I hope you support me more.

  • python
  • K-nearest neighbor
  • weights

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