具體包括的算法如下：

Model1Angle-basedOutlierDetector(ABOD)

Model2Cluster-basedLocalOutlierFactor(CBLOF)

Model3FeatureBagging

Model4Histogram-baseOutlierDetection(HBOS)

Model5IsolationForest

Model6KNearestNeighbors(KNN)

Model7AverageKNN

Model8MedianKNN

Model9LocalOutlierFactor(LOF)

Model10MinimumCovarianceDeterminant(MCD)

Model11One-classSVM(OCSVM)

Model12PrincipalComponentAnalysis(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：

fori,(clf_name,clf)inenumerate(classifiers.items()):

print()

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

#fitthedataandtagoutliers

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()

#plotthelevelslinesandthepoints

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],

['learneddecisionfunction','trueinliers','trueoutliers'],

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

loc='lowerright')

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

subplot.set_xlim((-7,7))

subplot.set_ylim((-7,7))