How to use nearest neighbours for Classification?

How to use nearest neighbours for Classification?

How to use nearest neighbours for Classification?

This recipe helps you use nearest neighbours for Classification

In [2]:
## How to use nearest neighbours for Classification
def Snippet_155():
    print(format('## How to use nearest neighbours for Classification','*^82'))

    import warnings

    # load libraries
    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,

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

***************## 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,
[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.
[0.3115727  0.35223881 0.32926829]


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

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