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DeleteAnomalies[{example1,example2,…}]

異常であるとみなされた exampleiが削除されたリストを与える．

DeleteAnomalies[LearnedDistribution[…],data]

指定されたAnomalyDetectorFunction[…]またはLearnedDistribution[…]を使って data の異常値を取り除く．

詳細とオプション
Details and Options Details and Options
例題  
例  
スコープ  
オプション  
AcceptanceThreshold  
Method  
アプリケーション  
関連項目
関連するガイド
関連リンク
履歴
引用書式

DeleteAnomalies

DeleteAnomalies[{example1,example2,…}]

異常であるとみなされた exampleiが削除されたリストを与える．

DeleteAnomalies[LearnedDistribution[…],data]

指定されたAnomalyDetectorFunction[…]またはLearnedDistribution[…]を使って data の異常値を取り除く．

詳細とオプション

  • DeleteAnomaliesは，数値，名義，画像等のさまざまなデータ型に使うことができる．
  • 各 exampleiは，単一のデータ要素，データ要素のリスト，あるいはデータ要素の連想でよい．例はDatasetオブジェクトまたはTabularオブジェクトとして与えることもできる．
  • DeleteAnomaliesは，異常（つまり「分布から外れる」例）を検出するために，異常がないデータの分布をモデル化しようとする．例のRarerProbabilityがAcceptanceThresholdで指定された値よりも低いとき，その例は異常であるとみなされる．
  • DeleteAnomalies[AnomalyDetectorFunction[…],data]の data が検出器の訓練例と同じ分布からのもののとき，AcceptanceThresholdは異常検出の偽陽性率に相当する．
  • 次は，使用可能なオプションである．
  • AcceptanceThreshold 0.001例を異常であるとみなすRarerProbability閾値
    FeatureExtractorIdentity学習する特徴をどのように抽出するか
    FeatureNamesAutomatic入力データに割り当てる特徴名
    FeatureTypesAutomatic入力データに仮定する特徴タイプ
    Method Automaticどのモデリングアルゴリズムを使用するか
    PerformanceGoalAutomatic最適化するパフォーマンスの局面
    RandomSeeding1234擬似乱数生成器の内部的シードをどのように行うか
    TimeGoalAutomatic検出器の訓練にどの程度の時間を費やするか
    TrainingProgressReportingAutomatic訓練中の進捗状況をどのように報告するか
    ValidationSetAutomatic訓練中にモデルの評価に使うデータ集合
  • 次は，PerformanceGoalの可能な設定である．
  • "Quality"検出器のモデリング品質を最高にする
    "Speed"異常を検出するスピードを最高にする
    Automaticスピード，品質，メモリの自動トレードオフ
    {goal1,goal2,…}goal1，goal2等を自動的に結合する
  • Methodの可能な設定はLearnDistribution[…]で与えられるものと同じである．
  • 次は，TrainingProgressReportingの使用可能な設定である．
  • "Panel"動的に更新されるグラフィカルなパネルを表示する
    "Print"Printを使って定期的に情報を報告する
    "ProgressIndicator"単純なProgressIndicatorを表示する
    "SimplePanel"学習曲線なしでパネルを動的に更新する
    None情報は何も報告しない
  • DeleteAnomalies[…,FeatureExtractor"Minimal"]は，内部的な前処理ができるだけ簡単に行われるべきであることを示す．

例題

すべて開く すべて閉じる

例  (3)

数値データ集合から異常な例を削除する：

Wolfram Language code: DeleteAnomalies[{1.2, 2.5, 3.2, 100, 4.6, 5, 5.1}]

名義的データ集合から異常な例を削除する：

Wolfram Language code: DeleteAnomalies[{{"A", 1}, {"A", 1.1}, {"B", 2}, {"B", 2.5}, {"C", 3}, {"C", 4}, {"C", 4.5}, {"C", 20}}]

色のリストから異常なものを削除する：

Wolfram Language code: DeleteAnomalies[{RGBColor[1, 0, 0], RGBColor[0, 0, 1], RGBColor[0, 1, 0], RGBColor[0.5659592309624889, 0.9770206495502198, 0.9985863063171145], RGBColor[0.3754327846016599, 0.7732937679156299, 0.9934076415002092], RGBColor[0.6161706939425584, -0.14481564113292222, 1.00353426972292], RGBColor[0.41787305847097933, 0.7295653444285688, 0.9971213930223727], RGBColor[0.38756575689146133, 0.8557000434145627, 0.9985713784934372], RGBColor[0.7237746394462758, 0.6864615128156799, 1.0049558953085165], RGBColor[0.4930933634567368, 1.0123748348501285, 0.9922647112874262], RGBColor[0.5083906475863398, 0.23671687553023163, 0.9933475855880759], RGBColor[0.5356308588856586, 0.9101219694870664, 0.9954192657779505], RGBColor[0.35371315176390594, 0.49763789511715706, 0.9991939268536737], RGBColor[0.5774616748672672, 0.43906208545640635, 1.0027712315253168], RGBColor[0.8301864069760181, 0.5690019228798344, 1.0080380129216597], RGBColor[0.7252770979304081, 0.6865831707732285, 0.9933529799272527], RGBColor[0.40911415636021287, 0.7226193316264145, 0.9966957402415179], RGBColor[0.8482079770014748, 0.8501425937052987, 0.9984394751245201], RGBColor[0.46509466164008856, 0.6496001687112135, 0.9943044940805594], RGBColor[0.5223381695947401, 0.9492448201228033, 0.9909519535914891], RGBColor[0.4056317599258231, 0.5979537199100144, 0.9960609147027923], RGBColor[0.5681282152142627, 0.6145506274472882, 0.9942053386481897], RGBColor[0.7340135990764531, 0.7363032159353553, 1.0051356464686494], RGBColor[0.568680351544366, 0.8178935615276761, 0.9972973909156875], RGBColor[0.8782992044353817, 0.5676721429387377, 0.9976822312013324], RGBColor[0.8912340489623893, 0.7418023277818317, 1.0056537818949918], RGBColor[0.5024620333700911, 0.5047283647090592, 0.9983530694802806], RGBColor[0.8078717924205181, 0.653899537453991, 0.9975680282006709], RGBColor[0.5263860266188425, 0.8891481475122383, 0.9967074371571546], RGBColor[0.4509497768400042, 0.9746743907386307, 0.9921412365780125], RGBColor[0.36709487152223774, 0.7030260587378026, 0.990996524067415], RGBColor[0.3486258167177018, 0.8782565717625381, 0.9924313945690186], RGBColor[0.2362783021030173, 1.0366668357638806, 0.9920321150148772], RGBColor[0.5037753263210084, 1.1372490894202187, 0.9970364159364161], RGBColor[0.48515674347257165, 0.623438767322434, 0.9987062896640942], RGBColor[0.309752273104561, 0.5516216087074916, 0.9979006130216704], RGBColor[0.791886226973098, 0.8555278868479104, 0.9964013988720439], RGBColor[0.36489380887741857, 0.6538971286146514, 0.9947083539138546], RGBColor[0.5788067899336176, 0.518500061180151, 1.0059894700291705], RGBColor[0.7591973658560403, 0.32754424645484553, 1.0031595845968302], RGBColor[0.611080929102045, 0.6093006791401225, 0.9968229964712378], RGBColor[0.7071196306427605, 0.6017237761211243, 1.002238791867162], RGBColor[0.607800621747923, 0.688729617245539, 0.9948471916926879], RGBColor[0.5228212118948214, 0.9621391171030302, 0.9989102310042973], RGBColor[0.4034263843628224, 0.6961712245533224, 0.9945334634229808], RGBColor[0.4624897911143086, 1.0117646816528425, 0.9976308623651218], RGBColor[0.22546551277377974, 0.49968908858854955, 0.9892293741848242], RGBColor[0.39610094617200603, 0.6406324106874157, 0.9958241193542212], RGBColor[0.2980675811368619, 0.7367494980194614, 0.9938982243446775], RGBColor[0.366116364693152, 0.5879590062582961, 0.9971279176250676], RGBColor[0.7627350568599146, 0.8758122841547133, 1.0006254489536797], RGBColor[0.5606336793064048, 0.7364374406357912, 0.9976736921570344], RGBColor[0.611217969265626, 0.4461420579128267, 0.9902433094513072], RGBColor[0.7156569387180267, 0.7123170061839005, 0.9962110198471927], RGBColor[0.6791440275858908, 0.9777377725744023, 0.9993352675306073], RGBColor[0.7535427926452951, 0.8140291014876614, 0.9999779030671445], RGBColor[0.4224730449691919, 0.7048172988308568, 1.000218197645998], RGBColor[1.0831053391201486, 0.5162623535933131, 1.0053053485793753], RGBColor[0.5908326301132645, 0.9331899955428621, 0.9993293758777952], RGBColor[0.4084175124285697, 0.5030906121830307, 0.9934012321919623], RGBColor[0.5906929070515831, 0.774539136049385, 1.0013136342783098], RGBColor[0.7279592862294082, 0.808024376838693, 0.990598201263683], RGBColor[0.4526619806424369, 0.8572369865359456, 0.992027772161482], RGBColor[0.6116378507223711, 0.9692792232881027, 1.0007282546056637], RGBColor[0.6728030121309461, 0.6159945503679183, 0.999994265026891], RGBColor[0.6187262817015476, 0.38329666431149445, 0.9983959873247816], RGBColor[0.5537139731404712, 1.00220867269463, 0.9971841114269797], RGBColor[0.5824929564854407, 0.8568652855691417, 0.9978052285631961], RGBColor[0.6467916416926835, 0.6989278893033707, 1.0040647924000072], RGBColor[0.4715405812654946, 0.48471583692401254, 0.9919917090290089], RGBColor[0.7501337350307926, 0.5478514012539901, 1.0001675930894274], RGBColor[0.7935860581270134, 0.0865495328323066, 1.0003613958535735], RGBColor[0.6957752871650936, 0.6750985254752383, 1.0028749839459759], RGBColor[0.5286599401622628, 0.752375592353536, 0.9924914932179304], RGBColor[0.7803166955792776, 0.6640679089392894, 1.0007720191828262], RGBColor[0.8149567117022473, 0.9107449749917345, 1.0018847201660235], RGBColor[0.28687387932890723, 0.5251822879837312, 1.0048453553240377], RGBColor[0.6339138155603266, 0.9499413635111722, 0.9957923550554846], RGBColor[0.6195935090234708, 0.9104921054525388, 0.9993262188604629], RGBColor[0.629437512360706, 0.7836610961087073, 0.9969560811106394], RGBColor[0.9539599691655274, 0.5727469496664113, 1.005124666562766], RGBColor[0.461135511071761, 0.7047561907538, 0.9971199066160322], RGBColor[0.6114235548448876, 1.052922842230812, 1.0000460379208387], RGBColor[0.8353121161086462, 0.4688421901010138, 1.0009262851959584], RGBColor[0.46781661731476154, 1.1396495538311269, 0.9890405978905257], RGBColor[0.8292770313746848, 0.4360701445302748, 1.0009047584693789], RGBColor[0.5170804874340198, 1.064408222280961, 1.0012411854402568], RGBColor[0.7650765408336083, 0.36936107116122646, 0.9945172422937401], RGBColor[0.45119945401989625, 0.856693556198354, 0.9957778134338929], RGBColor[0.39761045159524083, 0.6933289418577382, 0.9976912701802133], RGBColor[0.8159235329521966, 0.4299485471655765, 0.9984939390404028], RGBColor[0.357164361739695, 0.7217436860994018, 0.9966322520929208], RGBColor[0.5610070009904424, 0.8699407233165275, 0.9982375345503843], RGBColor[0.3854123195663661, 0.9861016334439296, 0.9907281406060822], RGBColor[0.6412771660095753, 0.5406870672701729, 1.0025080649577107], RGBColor[0.9426674051744555, 0.8144838170656485, 1.004881312262141], RGBColor[0.443342731164991, 0.8970900892291637, 0.9970488025463211], RGBColor[0.41572227417341934, 0.7900666387545331, 0.9906429085451064], RGBColor[0.6004982532903719, 1.0116685653582027, 0.9998841040044251], RGBColor[0.47986941698642815, 0.7665862166936707, 1.0001696262621942], RGBColor[0.7321221274856494, 0.7850360738337789, 0.9947731915575051], RGBColor[0.9012009151280851, 0.5175692646620489, 0.9983446734037713], RGBColor[0.7035730534656404, 0.46501229404822697, 0.9990571252937068]}]

スコープ  (3)

擬似実数乱数の二次元配列でAnomalyDetectorFunctionを訓練する：

Wolfram Language code: ad = AnomalyDetection[RandomReal[1, {20, 2}]]

訓練済みのAnomalyDetectorFunctionをDeleteAnomaliesと一緒に使って異常な例を除去する：

Wolfram Language code: DeleteAnomalies[ad, {{5, 0.6}, {0.3, 0.5}, {0.1, 0.2}, {5, 0.6}}]

Tabularオブジェクトから異常値を削除する：

Wolfram Language code: DeleteAnomalies@Tabular[Association["RawSchema" -> Association["ColumnProperties" -> Association["Value" -> Association["ElementType" -> "NumberExpression"]], "KeyColumns" -> None, "Backend" -> "WolframKernel"], "Options" -> {}, "BackendData" -> Association["ColumnData" -> DataStructure["ColumnTable", {{TabularColumn[Association["Data" -> {{-0.7, -0.3288, -0.515, -0.67, -0.17, 0.4, 100}, {}, None}, "ElementType" -> "NumberExpression"]]}}]]]]

画像の訓練と検証のデータ集合のランダムなサンプルを入手する：

Wolfram Language code: SeedRandom[123]; cifarsample = RandomSample[ResourceData["CIFAR-10"], 5000][[All, 1]]; {test, train} = TakeDrop[cifarsample, 100]; RandomSample[train, 10]

異常な例を加えてデータ集合が間違いを含むようにする：

Wolfram Language code: traincorup = Flatten[Join[Table[RandomImage[1, {12, 12}], 4], train ], 1]; testcorup = Flatten[Join[Table[RandomImage[1, {12, 12}], 4], test], 1]

分布を画像で訓練する：

Wolfram Language code: ldi = LearnDistribution[traincorup, PerformanceGoal -> "Quality"]

訓練済みの分布を使って検証集合に異常な例を含ませる：

Wolfram Language code: DeleteAnomalies[ldi, testcorup]

オプション  (2)

AcceptanceThreshold  (1)

色のリストからの異常値の削除にAcceptanceThresholdを指定する：

Wolfram Language code: DeleteAnomalies[{RGBColor[1, 0, 0], RGBColor[0, 0, 1], RGBColor[0, 1, 0], RGBColor[0.5659592309624889, 0.9770206495502198, 0.9985863063171145], RGBColor[0.3754327846016599, 0.7732937679156299, 0.9934076415002092], RGBColor[0.6161706939425584, -0.14481564113292222, 1.00353426972292], RGBColor[0.41787305847097933, 0.7295653444285688, 0.9971213930223727], RGBColor[0.38756575689146133, 0.8557000434145627, 0.9985713784934372], RGBColor[0.7237746394462758, 0.6864615128156799, 1.0049558953085165], RGBColor[0.4930933634567368, 1.0123748348501285, 0.9922647112874262], RGBColor[0.5083906475863398, 0.23671687553023163, 0.9933475855880759], RGBColor[0.5356308588856586, 0.9101219694870664, 0.9954192657779505], RGBColor[0.35371315176390594, 0.49763789511715706, 0.9991939268536737], RGBColor[0.5774616748672672, 0.43906208545640635, 1.0027712315253168], RGBColor[0.8301864069760181, 0.5690019228798344, 1.0080380129216597], RGBColor[0.7252770979304081, 0.6865831707732285, 0.9933529799272527], RGBColor[0.40911415636021287, 0.7226193316264145, 0.9966957402415179], RGBColor[0.8482079770014748, 0.8501425937052987, 0.9984394751245201], RGBColor[0.46509466164008856, 0.6496001687112135, 0.9943044940805594], RGBColor[0.5223381695947401, 0.9492448201228033, 0.9909519535914891], RGBColor[0.4056317599258231, 0.5979537199100144, 0.9960609147027923], RGBColor[0.5681282152142627, 0.6145506274472882, 0.9942053386481897], RGBColor[0.7340135990764531, 0.7363032159353553, 1.0051356464686494], RGBColor[0.568680351544366, 0.8178935615276761, 0.9972973909156875], RGBColor[0.8782992044353817, 0.5676721429387377, 0.9976822312013324], RGBColor[0.8912340489623893, 0.7418023277818317, 1.0056537818949918], RGBColor[0.5024620333700911, 0.5047283647090592, 0.9983530694802806], RGBColor[0.8078717924205181, 0.653899537453991, 0.9975680282006709], RGBColor[0.5263860266188425, 0.8891481475122383, 0.9967074371571546], RGBColor[0.4509497768400042, 0.9746743907386307, 0.9921412365780125], RGBColor[0.36709487152223774, 0.7030260587378026, 0.990996524067415], RGBColor[0.3486258167177018, 0.8782565717625381, 0.9924313945690186], RGBColor[0.2362783021030173, 1.0366668357638806, 0.9920321150148772], RGBColor[0.5037753263210084, 1.1372490894202187, 0.9970364159364161], RGBColor[0.48515674347257165, 0.623438767322434, 0.9987062896640942], RGBColor[0.309752273104561, 0.5516216087074916, 0.9979006130216704], RGBColor[0.791886226973098, 0.8555278868479104, 0.9964013988720439], RGBColor[0.36489380887741857, 0.6538971286146514, 0.9947083539138546], RGBColor[0.5788067899336176, 0.518500061180151, 1.0059894700291705], RGBColor[0.7591973658560403, 0.32754424645484553, 1.0031595845968302], RGBColor[0.611080929102045, 0.6093006791401225, 0.9968229964712378], RGBColor[0.7071196306427605, 0.6017237761211243, 1.002238791867162], RGBColor[0.607800621747923, 0.688729617245539, 0.9948471916926879], RGBColor[0.5228212118948214, 0.9621391171030302, 0.9989102310042973], RGBColor[0.4034263843628224, 0.6961712245533224, 0.9945334634229808], RGBColor[0.4624897911143086, 1.0117646816528425, 0.9976308623651218], RGBColor[0.22546551277377974, 0.49968908858854955, 0.9892293741848242], RGBColor[0.39610094617200603, 0.6406324106874157, 0.9958241193542212], RGBColor[0.2980675811368619, 0.7367494980194614, 0.9938982243446775], RGBColor[0.366116364693152, 0.5879590062582961, 0.9971279176250676], RGBColor[0.7627350568599146, 0.8758122841547133, 1.0006254489536797], RGBColor[0.5606336793064048, 0.7364374406357912, 0.9976736921570344], RGBColor[0.611217969265626, 0.4461420579128267, 0.9902433094513072], RGBColor[0.7156569387180267, 0.7123170061839005, 0.9962110198471927], RGBColor[0.6791440275858908, 0.9777377725744023, 0.9993352675306073], RGBColor[0.7535427926452951, 0.8140291014876614, 0.9999779030671445], RGBColor[0.4224730449691919, 0.7048172988308568, 1.000218197645998], RGBColor[1.0831053391201486, 0.5162623535933131, 1.0053053485793753], RGBColor[0.5908326301132645, 0.9331899955428621, 0.9993293758777952], RGBColor[0.4084175124285697, 0.5030906121830307, 0.9934012321919623], RGBColor[0.5906929070515831, 0.774539136049385, 1.0013136342783098], RGBColor[0.7279592862294082, 0.808024376838693, 0.990598201263683], RGBColor[0.4526619806424369, 0.8572369865359456, 0.992027772161482], RGBColor[0.6116378507223711, 0.9692792232881027, 1.0007282546056637], RGBColor[0.6728030121309461, 0.6159945503679183, 0.999994265026891], RGBColor[0.6187262817015476, 0.38329666431149445, 0.9983959873247816], RGBColor[0.5537139731404712, 1.00220867269463, 0.9971841114269797], RGBColor[0.5824929564854407, 0.8568652855691417, 0.9978052285631961], RGBColor[0.6467916416926835, 0.6989278893033707, 1.0040647924000072], RGBColor[0.4715405812654946, 0.48471583692401254, 0.9919917090290089], RGBColor[0.7501337350307926, 0.5478514012539901, 1.0001675930894274], RGBColor[0.7935860581270134, 0.0865495328323066, 1.0003613958535735], RGBColor[0.6957752871650936, 0.6750985254752383, 1.0028749839459759], RGBColor[0.5286599401622628, 0.752375592353536, 0.9924914932179304], RGBColor[0.7803166955792776, 0.6640679089392894, 1.0007720191828262], RGBColor[0.8149567117022473, 0.9107449749917345, 1.0018847201660235], RGBColor[0.28687387932890723, 0.5251822879837312, 1.0048453553240377], RGBColor[0.6339138155603266, 0.9499413635111722, 0.9957923550554846], RGBColor[0.6195935090234708, 0.9104921054525388, 0.9993262188604629], RGBColor[0.629437512360706, 0.7836610961087073, 0.9969560811106394], RGBColor[0.9539599691655274, 0.5727469496664113, 1.005124666562766], RGBColor[0.461135511071761, 0.7047561907538, 0.9971199066160322], RGBColor[0.6114235548448876, 1.052922842230812, 1.0000460379208387], RGBColor[0.8353121161086462, 0.4688421901010138, 1.0009262851959584], RGBColor[0.46781661731476154, 1.1396495538311269, 0.9890405978905257], RGBColor[0.8292770313746848, 0.4360701445302748, 1.0009047584693789], RGBColor[0.5170804874340198, 1.064408222280961, 1.0012411854402568], RGBColor[0.7650765408336083, 0.36936107116122646, 0.9945172422937401], RGBColor[0.45119945401989625, 0.856693556198354, 0.9957778134338929], RGBColor[0.39761045159524083, 0.6933289418577382, 0.9976912701802133], RGBColor[0.8159235329521966, 0.4299485471655765, 0.9984939390404028], RGBColor[0.357164361739695, 0.7217436860994018, 0.9966322520929208], RGBColor[0.5610070009904424, 0.8699407233165275, 0.9982375345503843], RGBColor[0.3854123195663661, 0.9861016334439296, 0.9907281406060822], RGBColor[0.6412771660095753, 0.5406870672701729, 1.0025080649577107], RGBColor[0.9426674051744555, 0.8144838170656485, 1.004881312262141], RGBColor[0.443342731164991, 0.8970900892291637, 0.9970488025463211], RGBColor[0.41572227417341934, 0.7900666387545331, 0.9906429085451064], RGBColor[0.6004982532903719, 1.0116685653582027, 0.9998841040044251], RGBColor[0.47986941698642815, 0.7665862166936707, 1.0001696262621942], RGBColor[0.7321221274856494, 0.7850360738337789, 0.9947731915575051], RGBColor[0.9012009151280851, 0.5175692646620489, 0.9983446734037713], RGBColor[0.7035730534656404, 0.46501229404822697, 0.9990571252937068]}, AcceptanceThreshold -> 0.5]

Method  (1)

異なる2つの分布からサンプルを取ってデータ集合を作る：

Wolfram Language code: data = Join[RandomVariate[NormalDistribution[-1, 0.5], 70], RandomVariate[ChiDistribution[6], 2]]

データ集合中の異常値を"Multinormal"メソッドを使って削除する：

Wolfram Language code: DeleteAnomalies[data, Method -> "Multinormal"]

データ集合中の異常値を"KernelDensityEstimation"メソッドを使って削除する：

Wolfram Language code: DeleteAnomalies[data, Method -> "KernelDensityEstimation"]

アプリケーション  (4)

異常値を含む数値の統計的平均を求める：

Wolfram Language code: Mean[{1.2, 2.2, 3.4, 4.1, 200, 5.7, 6, 7, 8, 8.3}]

平均を計算する前に異常値を削除する：

Wolfram Language code: Mean[DeleteAnomalies[{1.2, 2.2, 3.4, 4.1, 200, 5.7, 6, 7, 8, 8.3}]]

異常値を含む数値のリストをプロットする：

Wolfram Language code: data = {2, 1, 3, 100, 2, 1, 2, 1000, 6, 2, 5, 8, 15}; ListLinePlot[data, PlotRange -> All]

数値のリストを異常値を除いてからプロットする：

Wolfram Language code: ListLinePlot[DeleteAnomalies[data]]

数値データの線形フィットを求める：

Wolfram Language code: data = {{0, 1}, {1, 0}, {3, 2}, {5, 4}, {6, 7}, {200, 8}};
Wolfram Language code: LinearModelFit[data, x, x]

フィットをモデル化する前に外れ値を削除する：

Wolfram Language code: LinearModelFit[DeleteAnomalies[data], x, x]

画像のデータ集合を入手する：

Wolfram Language code: train = RandomSample[ResourceData["MNIST", "TrainingData"][[All, 1]], 30000];
Wolfram Language code: RandomSample[train, 10]

訓練集合で異常検出器を訓練する：

Wolfram Language code: ad = AnomalyDetection[train, Method -> "Multinormal"]

検定集合中の異常な例を除去する：

Wolfram Language code: DeleteAnomalies[ad, {[image], [image], [image], [image], [image], [image], [image], [image], [image], [image], [image], [image], [image], [image], [image], [image], [image], [image], [image], [image], [image], [image], [image], [image], [image], [image], [image], [image], [image], [image], [image], [image], [image], [image], [image], [image], [image], [image]}]
Wolfram Research (2019), DeleteAnomalies, Wolfram言語関数， https://reference.wolfram.com/language/ref/DeleteAnomalies.html （2025年に更新）.

テキスト

Wolfram Research (2019), DeleteAnomalies, Wolfram言語関数， https://reference.wolfram.com/language/ref/DeleteAnomalies.html （2025年に更新）.

CMS

Wolfram Language. 2019. "DeleteAnomalies." Wolfram Language & System Documentation Center. Wolfram Research. Last Modified 2025. https://reference.wolfram.com/language/ref/DeleteAnomalies.html.

APA

Wolfram Language. (2019). DeleteAnomalies. Wolfram Language & System Documentation Center. Retrieved from https://reference.wolfram.com/language/ref/DeleteAnomalies.html

BibTeX

@misc{reference.wolfram_2026_deleteanomalies, author="Wolfram Research", title="{DeleteAnomalies}", year="2025", howpublished="\url{https://reference.wolfram.com/language/ref/DeleteAnomalies.html}", note=[Accessed: 14-September-2026]}

BibLaTeX

@online{reference.wolfram_2026_deleteanomalies, organization={Wolfram Research}, title={DeleteAnomalies}, year={2025}, url={https://reference.wolfram.com/language/ref/DeleteAnomalies.html}, note=[Accessed: 14-September-2026]}