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

This recipe helps you use nearest neighbours for Classification

0
In [2]:
```## How to use nearest neighbours for Classification
def Snippet_155():
print()
print(format('## How to use nearest neighbours for Classification','*^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_classification(n_samples=1000, n_features=20, n_informative=5,
n_redundant=2, n_repeated=0, n_classes=10, n_clusters_per_class=2,
weights=None, flip_y=0.01, class_sep=1.0, hypercube=True, shift=0.0,
scale=1.0, shuffle=True, random_state=None)
X = dataset[0]
y = dataset[1]

# Create an scaler object
sc = StandardScaler()
# Create a pca object
pca = decomposition.PCA()
# Create a logistic regression object with an L2 penalty
KNN = neighbors.KNeighborsClassifier()
# 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]+1,1))
# Create lists of parameter for KNeighborsRegressor()
n_neighbors = [2, 3, 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='accuracy', verbose=1)
print(); print(CV_Result)
print(); print(CV_Result.mean())
print(); print(CV_Result.std())

Snippet_155()
```
```***************## How to use nearest neighbours for Classification****************
Best Number Of Components: 17

KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
metric_params=None, n_jobs=None, n_neighbors=10, p=2,
weights='uniform')
```
```[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.
```
```[0.3115727  0.35223881 0.32926829]

0.3310265996499373

0.01664835868192384
```
```[Parallel(n_jobs=-1)]: Done   3 out of   3 | elapsed:    9.4s finished
```

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