How to use nearest neighbours for Regression?
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# How to use nearest neighbours for Regression?

This recipe helps you use nearest neighbours for Regression

0
In :
```## How to use nearest neighbours for Regression
def Snippet_154():
print()
print(format('## How to use nearest neighbours for Regression','*^82'))

import warnings
warnings.filterwarnings("ignore")

from sklearn import decomposition, datasets
from sklearn import neighbors
from sklearn.pipeline import Pipeline
from sklearn.model_selection import GridSearchCV, cross_val_score
from sklearn.preprocessing import StandardScaler

# Load the iris flower data
dataset = datasets.make_regression(n_samples=1000, n_features=20, n_informative=10,
n_targets=1, bias=0.0, effective_rank=None, tail_strength=0.5, noise=0.0,
shuffle=True, coef=False, random_state=None)
X = dataset
y = dataset

# Create an scaler object
sc = StandardScaler()

# Create a pca object
pca = decomposition.PCA()

# Create a logistic regression object with an L2 penalty
KNN = neighbors.KNeighborsRegressor()

# Create a pipeline of three steps. First, standardize the data.
# Second, tranform the data with PCA.
# Third, train a Decision Tree Classifier on the data.
pipe = Pipeline(steps=[('sc', sc),
('pca', pca),
('KNN', KNN)])

# Create Parameter Space
# Create a list of a sequence of integers from 1 to 30 (the number of features in X + 1)
n_components = list(range(1,X.shape+1,1))

# Create lists of parameter for KNeighborsRegressor()
n_neighbors = [5, 10]
algorithm = ['auto',  'ball_tree', 'kd_tree', 'brute']

# Create a dictionary of all the parameter options
# Note has you can access the parameters of steps of a pipeline by using '__’
parameters = dict(pca__n_components=n_components,
KNN__n_neighbors=n_neighbors,
KNN__algorithm=algorithm)

# Conduct Parameter Optmization With Pipeline
# Create a grid search object
clf = GridSearchCV(pipe, parameters)

# Fit the grid search
clf.fit(X, y)

# View The Best Parameters
print('Best Number Of Components:', clf.best_estimator_.get_params()['pca__n_components'])
print(); print(clf.best_estimator_.get_params()['KNN'])

# Use Cross Validation To Evaluate Model
CV_Result = cross_val_score(clf, X, y, cv=3, n_jobs=-1, scoring='r2')
print(); print(CV_Result)
print(); print(CV_Result.mean())
print(); print(CV_Result.std())

Snippet_154()
```
```*****************## How to use nearest neighbours for Regression******************
Best Number Of Components: 17

KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',
metric_params=None, n_jobs=None, n_neighbors=10, p=2,
weights='uniform')

[0.61096235 0.62923981 0.58886741]

0.6096898558628526

0.016506503277788975
```

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