了解離群值以及如何使用Python中的PyOD檢測離群值？

具體包括的算法如下：

Model 1 Angle-based Outlier Detector (ABOD)

Model 2 Cluster-based Local Outlier Factor (CBLOF)

Model 3 Feature Bagging

Model 4 Histogram-base Outlier Detection (HBOS)

Model 5 Isolation Forest

Model 6 K Nearest Neighbors (KNN)

Model 7 Average KNN

Model 8 Median KNN

Model 9 Local Outlier Factor (LOF)

Model 10 Minimum Covariance Determinant (MCD)

Model 11 One-class SVM (OCSVM)

Model 12 Principal Component Analysis (PCA)

這些算法主要都是無監督的方式來實現的異常離群點值檢測的方法。

同時也提供了對所有算法的比較：

其核心代碼如下：

for i, (clf_name, clf) in enumerate(classifiers.items()):

print()

print(i + 1, 'fitting', clf_name)

# fit the data and tag outliers

clf.fit(X)

scores_pred = clf.decision_function(X) * -1

y_pred = clf.predict(X)

threshold = stats.scoreatpercentile(scores_pred,

100 * outliers_fraction)

n_errors = (y_pred != ground_truth).sum()

# plot the levels lines and the points

Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()]) * -1

Z = Z.reshape(xx.shape)

subplot = plt.subplot(3, 4, i + 1)

subplot.contourf(xx, yy, Z, levels=np.linspace(Z.min(), threshold, 7),

cmap=plt.cm.Blues_r)

a = subplot.contour(xx, yy, Z, levels=[threshold],

linewidths=2, colors='red')

subplot.contourf(xx, yy, Z, levels=[threshold, Z.max()],

colors='orange')

b = subplot.scatter(X[:-n_outliers, 0], X[:-n_outliers, 1], c='white',

s=20, edgecolor='k')

c = subplot.scatter(X[-n_outliers:, 0], X[-n_outliers:, 1], c='black',

s=20, edgecolor='k')

subplot.axis('tight')

subplot.legend(

[a.collections[0], b, c],

['learned decision function', 'true inliers', 'true outliers'],

prop=matplotlib.font_manager.FontProperties(size=10),

loc='lower right')

subplot.set_xlabel("%d. %s (errors: %d)" % (i + 1, clf_name, n_errors))

subplot.set_xlim((-7, 7))

subplot.set_ylim((-7, 7))