Nearest Neighbors Classification
此範例使用Nearest Neighbors Classification (簡稱NNC 近鄰分類法)將數據分類,並繪製出各類別的decision boundaries(決策邊界)
NNC計算的基礎是「物以類聚」,換句話說,同類型的資料應該會聚集在一起,若以座標中的點來表示,則這些點的距離應該會比較接近。因此,對於一筆未被標籤的資料,我們只要找出在訓練集中和此筆資料最接近的點,就可以判定此筆資料的類別應該和最接近的點的類別是一樣的。NNC是一個較直覺的分類法,在測試各種分類器時,常被當成是基礎的分類器,以便和其他更複雜的分類器進行效能比較。

(一)引入函式庫

    numpy : 產生陣列數值
    matplotlib.pyplot : 用來繪製影像
    matplotlib.colors import ListedColormap : 匯入用來生成圖上的顏色表
    sklearn import neighbors, datasets : 匯入NNC及資料集
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    import numpy as np
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    import matplotlib.pyplot as plt
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    from matplotlib.colors import ListedColormap
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    from sklearn import neighbors, datasets
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    (二)匯入資料集
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n_neighbors = 15 # 用於NNC函式內的變數
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# import some data to play with
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iris = datasets.load_iris() # 匯入資料集
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# we only take the first two features. We could avoid this ugly
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# slicing by using a two-dim dataset
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X = iris.data[:, :2] # 只取資料的前2項數據
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y = iris.target # 分類的標籤
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h = .02 # step size in the mesh
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(三)繪製結果圖

    neighbors.KNeighborsClassifier(n_neighbors=5, weights='uniform', algorithm='auto', leaf_size=30, p=2, metric='minkowski', metric_params=None, n_jobs=None, **kwargs)
    n_neighbors : 近鄰查詢的鄰居數
    weights : 用於預測的權重函數。'uniform' 每個點都被平均加權 'distance' 權重是點之間距離的倒數。在這種情況下,查詢點的近鄰比遠處的近鄰具有更大的影響
    algorithm{‘auto’, ‘ball_tree’, ‘kd_tree’, ‘brute’} : 用於計算近鄰的算法。
    np.meshgrid() : 從給定的座標向量回傳座標矩陣
    ```python
    Create color maps
    cmap_light = ListedColormap(['orange', 'cyan', 'cornflowerblue'])
    cmap_bold = ListedColormap(['darkorange', 'c', 'darkblue'])
for weights in ['uniform', 'distance']:
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# we create an instance of Neighbours Classifier and fit the data.
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clf = neighbors.KNeighborsClassifier(n_neighbors, weights=weights) # 使用NNC函式
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clf.fit(X, y) # 擬合資料集
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# Plot the decision boundary. For that, we will assign a color to each
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# point in the mesh [x_min, x_max]x[y_min, y_max].
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x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1 # 設定mesh x的大小邊界
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y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1 # 設定mesh y的大小邊界
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xx, yy = np.meshgrid(np.arange(x_min, x_max, h),
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np.arange(y_min, y_max, h))
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Z = clf.predict(np.c_[xx.ravel(), yy.ravel()]) # 預測資料的分類
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# Put the result into a color plot
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Z = Z.reshape(xx.shape)
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plt.figure()
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plt.pcolormesh(xx, yy, Z, cmap=cmap_light)
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# Plot also the training points
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plt.scatter(X[:, 0], X[:, 1], c=y, cmap=cmap_bold,
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edgecolor='k', s=20)
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plt.xlim(xx.min(), xx.max())
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plt.ylim(yy.min(), yy.max())
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plt.title("3-Class classification (k = %i, weights = '%s')"
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% (n_neighbors, weights))
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plt.show()
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![](https://github.com/sdgary56249128/machine-learning-python/blob/master/Nearest_Neighbors/sphx_glr_plot_classification_001.png)
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![](https://github.com/sdgary56249128/machine-learning-python/blob/master/Nearest_Neighbors/sphx_glr_plot_classification_002.png)
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https://scikit-learn.org/stable/_downloads/fb5fbc2d9b876b776e016c37233e76fd/plot_classification.py
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```python
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print(__doc__)
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import numpy as np
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import matplotlib.pyplot as plt
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from matplotlib.colors import ListedColormap
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from sklearn import neighbors, datasets
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n_neighbors = 15
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# import some data to play with
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iris = datasets.load_iris()
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# we only take the first two features. We could avoid this ugly
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# slicing by using a two-dim dataset
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X = iris.data[:, :2]
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y = iris.target
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h = .02 # step size in the mesh
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# Create color maps
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cmap_light = ListedColormap(['orange', 'cyan', 'cornflowerblue'])
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cmap_bold = ListedColormap(['darkorange', 'c', 'darkblue'])
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for weights in ['uniform', 'distance']:
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# we create an instance of Neighbours Classifier and fit the data.
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clf = neighbors.KNeighborsClassifier(n_neighbors, weights=weights)
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clf.fit(X, y)
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# Plot the decision boundary. For that, we will assign a color to each
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# point in the mesh [x_min, x_max]x[y_min, y_max].
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x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
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y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
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xx, yy = np.meshgrid(np.arange(x_min, x_max, h),
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np.arange(y_min, y_max, h))
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Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
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# Put the result into a color plot
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Z = Z.reshape(xx.shape)
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plt.figure()
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plt.pcolormesh(xx, yy, Z, cmap=cmap_light)
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# Plot also the training points
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plt.scatter(X[:, 0], X[:, 1], c=y, cmap=cmap_bold,
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edgecolor='k', s=20)
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plt.xlim(xx.min(), xx.max())
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plt.ylim(yy.min(), yy.max())
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plt.title("3-Class classification (k = %i, weights = '%s')"
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% (n_neighbors, weights))
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plt.show()
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Last modified 1yr ago