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{
"root": {
"data": {
"id": "cpd5frjk61c0",
"created": 1672200027513,
"text": "MainTopic",
"font-family": "sans-serif",
"expandState": "expand",
"hyperlink": "https://gkentei.techblog.jp/archives/13712572.html",
"hyperlinkTitle": "用語集 インデックス"
},
"children": [
{
"data": {
"id": "cpd75pkoay80",
"created": 1672204881805,
"text": "機械学習 ML\n(Machine Learning)",
"font-family": "sans-serif",
"hyperlink": "https://atmarkit.itmedia.co.jp/ait/series/14323/",
"hyperlinkTitle": "AI・機械学習の用語辞典"
},
"children": [
{
"data": {
"id": "cpd7kcsun7c0",
"created": 1672206029464,
"text": "教師あり学習\n(supervised learning)",
"font-family": "sans-serif"
},
"children": [
{
"data": {
"id": "cpea0e30s8w0",
"created": 1672314488258,
"text": "サポートベクターマシン SVM\n(Support Vector Machine)",
"font-family": "sans-serif"
},
"children": [
{
"data": {
"id": "cptnt9rikzs0",
"created": 1673876825195,
"text": "カーネル法 (Kernel method)",
"font-family": "sans-serif"
},
"children": []
},
{
"data": {
"id": "cptntch9tmg0",
"created": 1673876831106,
"text": "カーネルトリック (kernel trick)",
"font-family": "sans-serif"
},
"children": []
},
{
"data": {
"id": "cptntf5v27s0",
"created": 1673876836947,
"text": "スラック変数 (slack variable)",
"font-family": "sans-serif",
"hyperlink": "https://sudillap.hatenablog.com/entry/2013/04/08/235602",
"hyperlinkTitle": "サポートベクターマシンとは［ソフトマージンサポートベクターマシン］"
},
"children": []
}
]
},
{
"data": {
"id": "cpfs5eowj8w0",
"created": 1672467221338,
"text": "ロジスティック回帰\n(Logistic Regression)",
"font-family": "sans-serif",
"hyperlink": "https://asanoucla.github.io/R33_logistic1.html",
"hyperlinkTitle": ""
},
"children": []
},
{
"data": {
"id": "cpfv7qi5zc00",
"created": 1672475867109,
"text": "k近傍法 k-NN\n(k-nearest neighbor algorithm)",
"font-family": "sans-serif",
"note": "近傍にいる点と同じクラスに分類する\n"
},
"children": []
}
]
},
{
"data": {
"id": "cpd7kh5g5wg0",
"created": 1672206038932,
"text": "教師なし学習\n(unsupervised learning)",
"font-family": "sans-serif"
},
"children": [
{
"data": {
"id": "cpf6a299c7s0",
"created": 1672405521673,
"text": "t-SNE 法\n(t-distribution Stochastic Neighbor Embedding)",
"note": "高次元での距離の遠近を保ったまま、低次元空間で点を配置しなおす\n\nt：t分布\nS：Stochastic(確率的)\nN：Neighbor(隣接)\nE：Embedding(埋め込み)\n",
"font-family": "sans-serif",
"hyperlink": "https://blog.albert2005.co.jp/2015/12/02/tsne/",
"hyperlinkTitle": "t-SNE を用いた次元圧縮方法のご紹介"
},
"children": [
{
"data": {
"id": "cq54vmw7m080",
"created": 1675042128897,
"text": "次元削減\n(Dimensionality Reduction)",
"font-family": "sans-serif"
},
"children": []
}
]
},
{
"data": {
"id": "cpfs5lwq48g0",
"created": 1672467237048,
"text": "k平均法\n(k-means algorithm)",
"font-family": "sans-serif",
"hyperlink": "https://di-acc2.com/programming/python/4235/",
"hyperlinkTitle": "【AI・機械学習】k平均法(k-menas)によるモデル構築を解説｜Python活用の教師なしクラスタリング"
},
"children": []
},
{
"data": {
"id": "cpo7imnni9c0",
"created": 1673323053712,
"text": "ウォード法\n(Ward method)",
"font-family": "sans-serif",
"hyperlink": "https://mathwords.net/wardmethod",
"hyperlinkTitle": "ウォード法によるクラスタリングのやり方"
},
"children": []
},
{
"data": {
"id": "cq6uctmtnq00",
"created": 1675215563462,
"text": "多変量解析\n(multivariate analysis)",
"font-family": "sans-serif"
},
"children": [
{
"data": {
"id": "cpu1hml5eiw0",
"created": 1673915408277,
"text": "多次元尺度構成法 MDS\n(Multi-Dimensional Scaling)",
"font-family": "sans-serif",
"hyperlink": "https://www.business-research-lab.com/220404-3/",
"hyperlinkTitle": "多次元尺度法の基本的な考え方"
},
"children": [
{
"data": {
"id": "cq6tgsik69s0",
"created": 1675213053374,
"text": "対応分析 CA\n(Correspondence Analysis)",
"font-family": "sans-serif",
"hyperlink": "https://www.intage.co.jp/glossary/400/",
"hyperlinkTitle": "コレスポンデンス分析とは"
},
"children": []
}
]
},
{
"data": {
"id": "cpo7rnnubzk0",
"created": 1673323761178,
"text": "主成分分析 PCA\n(Principal Component Analysis)",
"hyperlink": "https://qiita.com/oki_kosuke/items/70c7e0bcd7b534589f69",
"hyperlinkTitle": "【初心者向け】主成分分析（PCA）って一体何をしているの？（理論編）",
"font-family": "sans-serif"
},
"children": []
},
{
"data": {
"id": "cq6tgnqibj40",
"created": 1675213042971,
"text": "独立成分分析 ICA\n(independent component analysis)",
"font-family": "sans-serif",
"hyperlink": "https://www.hellocybernetics.tech/entry/2016/05/01/185044",
"hyperlinkTitle": "独立成分分析の基礎と主成分分析との比較"
},
"children": []
}
]
},
{
"data": {
"id": "cq6th08ogaw0",
"created": 1675213070191,
"text": "自己組織化マップ SOM\n(Self-Organizing Map)",
"font-family": "sans-serif",
"note": "Kohonen's self-organizing MAP\nコホーネンの自己組織化マップ\n",
"hyperlink": "http://gaya.jp/spiking_neuron/som.htm",
"hyperlinkTitle": ""
},
"children": []
}
]
},
{
"data": {
"id": "cpd7kku8r8g0",
"created": 1672206046962,
"text": "半教師あり学習\n(Semi-Supervised Learning)",
"font-family": "sans-serif",
"hyperlink": "https://products.sint.co.jp/aisia/blog/vol1-20",
"hyperlinkTitle": ""
},
"children": []
},
{
"data": {
"id": "cpe9jkfxstc0",
"created": 1672313169909,
"text": "分類・回帰",
"font-family": "sans-serif"
},
"children": [
{
"data": {
"id": "cpf1y4xvqw00",
"created": 1672393302706,
"text": "分類\n(Classify)",
"font-family": "sans-serif"
},
"children": [
{
"data": {
"id": "cpfs5tzxm4o0",
"created": 1672467254656,
"text": "2値分類問題\n(Binary classification)",
"font-family": "sans-serif"
},
"children": [
{
"data": {
"id": "cpqocuhxeu00",
"created": 1673573679377,
"text": "決定境界\n(Decision Boundary)",
"font-family": "sans-serif"
},
"children": []
},
{
"data": {
"id": "cpd7imlt4280",
"created": 1672205894077,
"text": "混同行列\n(Confusion Matrix)",
"font-family": "sans-serif",
"note": "●真陽性 (TP)\n●真陰性 (TN)\n●偽陽性 (FP)\n●偽陰性 (FN)\n",
"hyperlink": "https://atmarkit.itmedia.co.jp/ait/articles/2208/08/news034.html",
"hyperlinkTitle": "［評価指標］混同行列（Confusion Matrix）とは？"
},
"children": [
{
"data": {
"id": "cpd83sd9tw80",
"created": 1672207552269,
"text": "正解率 (Accuracy)",
"font-family": "sans-serif",
"note": "(TP+TN)/(TP+TN+FP+FN)"
},
"children": []
},
{
"data": {
"id": "cpd8261lolc0",
"created": 1672207425310,
"text": "適合率 (Precision)",
"font-family": "sans-serif",
"note": "陽性と予測して陽性だった割合\nTP/(TP+FP)"
},
"children": []
},
{
"data": {
"id": "cpd834x7zbc0",
"created": 1672207501233,
"text": "再現率 (Recall)／\n感度 (Sensitivity)",
"note": "陽性であるもののうち、陽性であると予測できた割合\nTP/(TP+FN)\n",
"font-family": "sans-serif",
"hyperlink": "https://atmarkit.itmedia.co.jp/ait/articles/2209/29/news048.html",
"hyperlinkTitle": "［評価指標］再現率（Recall）／感度（Sensitivity）とは？"
},
"children": []
},
{
"data": {
"id": "cpf4k9bfxxk0",
"created": 1672400678464,
"text": "特異率 (Specificity)",
"font-family": "sans-serif",
"note": "陰性であるもののうち、陰性であると予測できた割合\nTN/(FP+TN)"
},
"children": []
},
{
"data": {
"id": "cpd85t9xgw80",
"created": 1672207710972,
"text": "F1値 (F1 score)",
"note": "適合率と再現率の調和平均\n(2 * 適合率 * 再現率) / (適合率 + 再現率)\n",
"font-family": "sans-serif"
},
"children": []
},
{
"data": {
"id": "cpd8mf63snk0",
"created": 1672209012457,
"text": "帰無仮説＝差がない\n(null hypothesis)",
"font-family": "sans-serif"
},
"children": [
{
"data": {
"id": "cpd7b486u9c0",
"created": 1672205305522,
"text": "第一種の過誤＝偽陽性\n(Type I error)",
"note": "帰無仮説「差がない」が正しいのに、誤って帰無仮説を棄却\n",
"font-family": "sans-serif"
},
"children": []
}
]
},
{
"data": {
"id": "cpd8mti4ipc0",
"created": 1672209043659,
"text": "対立仮説＝差がある\n(alternative hypothesis)",
"font-family": "sans-serif"
},
"children": [
{
"data": {
"id": "cpd7bhqgbeo0",
"created": 1672205334925,
"text": "第二種の過誤＝偽陰性\n(Type Ⅱ error)",
"note": "対立仮説「差がある」が正しいのに、帰無仮説を棄却できない\n",
"font-family": "sans-serif"
},
"children": []
}
]
},
{
"data": {
"id": "cq11efgmc800",
"created": 1674626077370,
"text": "ROC曲線 ROC curve\n(Receiver Operatorating Characteristic curve)",
"note": "●受信者動作特性曲線\n\n横軸 偽陽性率\n縦軸 敏感度\nでプロット\n",
"font-family": "sans-serif",
"hyperlink": "https://www.cresco.co.jp/blog/entry/15337/",
"hyperlinkTitle": "白黒はっきりしない判定の評価のしかた 〜ROC曲線と AUC〜"
},
"children": [
{
"data": {
"id": "cq11epo354g0",
"created": 1674626099589,
"text": "AUC\n(Area Under the ROC Curve)",
"font-family": "sans-serif",
"note": "ROC曲線の積分値\n理想は1\n",
"hyperlink": "https://atmarkit.itmedia.co.jp/ait/articles/2211/24/news019.html",
"hyperlinkTitle": "［評価指標］AUC（Area Under the ROC Curve：ROC曲線の下の面積）とは？"
},
"children": []
}
]
}
]
}
]
},
{
"data": {
"id": "cpe9yl1lxsw0",
"created": 1672314346682,
"text": "決定木\n(Decision Tree)",
"font-family": "sans-serif",
"note": "●分類木(Classification Tree)\nクラス分類\n\n●回帰木(Regression Tree)\n数値の予測\n",
"hyperlink": "https://en.wikipedia.org/wiki/Decision_tree",
"hyperlinkTitle": ""
},
"children": [
{
"data": {
"id": "cpqv8vo1new0",
"created": 1673593116236,
"text": "樹形図 デンドログラム\n(Dendrogram)",
"font-family": "sans-serif"
},
"children": []
},
{
"data": {
"id": "cpsl6v3e52w0",
"created": 1673767867073,
"text": "ジニ不純度 (Gini impurity)",
"font-family": "sans-serif",
"hyperlink": "https://datawokagaku.com/decision_tree_classifier/",
"hyperlinkTitle": "【決定木】ジニ不純度と木の剪定(cost complexity pruning)を解説【機械学習入門29】"
},
"children": []
},
{
"data": {
"id": "cq0ui2icjyo0",
"created": 1674606614864,
"text": "Mini-Max法\n(mini-max method)",
"font-family": "sans-serif"
},
"children": [
{
"data": {
"id": "cq0uil80qqg0",
"created": 1674606655598,
"text": "αβ法\n(alpha-beta pruning)",
"font-family": "sans-serif",
"note": "Mini-Max法による探索をできるだけ減らす\n\n・αカット\n・βカット\n\n",
"hyperlink": "https://pictblog.com/gmini-max",
"hyperlinkTitle": "【G検定】Mini-Max法とαβ法～具体例でわかりやすい解説を試みてみた～"
},
"children": []
}
]
}
]
},
{
"data": {
"id": "cq2wgt5hdj40",
"created": 1674815278264,
"text": "単純ベイズ／ナイーブベイズ\n(Naive Bayes)",
"font-family": "sans-serif",
"hyperlink": "https://analysis-navi.com/?p=3108",
"hyperlinkTitle": "ナイーブベイズ分類器の仕組み"
},
"children": []
},
{
"data": {
"id": "cphrmuxu4og0",
"created": 1672668887700,
"text": "交差エントロピー誤差\n(Cross-entropy Loss)",
"font-family": "sans-serif",
"note": "●クロスエントロピー\n",
"hyperlink": "https://qiita.com/kenta1984/items/59a9ef1788e6934fd962",
"hyperlinkTitle": "交差エントロピー誤差をわかりやすく説明してみる"
},
"children": []
}
]
},
{
"data": {
"id": "cpealn3muso0",
"created": 1672316153534,
"text": "回帰\n(Regression)",
"font-family": "sans-serif"
},
"children": [
{
"data": {
"id": "cpu15rcb38g0",
"created": 1673914478256,
"text": "線形回帰\n(Linear Regression)",
"font-family": "sans-serif"
},
"children": [
{
"data": {
"id": "cq4fj4jh2hc0",
"created": 1674970620827,
"text": "目的変数\n(objective variable)",
"font-family": "sans-serif",
"note": "予想したい値\n"
},
"children": []
},
{
"data": {
"id": "cq4fjc8cdls0",
"created": 1674970637569,
"text": "説明変数\n(explanatory variable)",
"font-family": "sans-serif",
"note": "目的変数を導くのに関係する値\n"
},
"children": []
},
{
"data": {
"id": "cq4fkrkuueo0",
"created": 1674970749341,
"text": "単回帰分析\n(Simple Linear Regression)",
"font-family": "sans-serif",
"note": "y = w0 + w1x1\n"
},
"children": []
},
{
"data": {
"id": "cpqo9tnc4bs0",
"created": 1673573442435,
"text": "重回帰分析\n(Multivariate Linear Regression)",
"font-family": "sans-serif",
"note": "y = w0 + w1x1 + w2x2 + … + wMxM\n",
"hyperlink": "https://www.asanoucla.com/%E8%A8%88%E9%87%8F%E5%88%86%E6%9E%90-%E8%A3%9C%E5%8A%A9%E6%95%99%E6%9D%90-r/",
"hyperlinkTitle": ""
},
"children": [
{
"data": {
"id": "cpxn7l5gn8g0",
"created": 1674281365798,
"text": "脱落変数バイアス OVB\n(Omitted Variable Bias)",
"font-family": "sans-serif",
"note": "必要な説明変数を含めない時\n",
"hyperlink": "http://www.ner.takushoku-u.ac.jp/masano/class_material/waseda/keiryo/R25_reg8_OBV.html",
"hyperlinkTitle": ""
},
"children": []
},
{
"data": {
"id": "cpxn7vsw6l40",
"created": 1674281388983,
"text": "処置後変数バイアス\n(post treatment variable bias)",
"font-family": "sans-serif",
"note": "入れるべきでない変数を含めた時\n"
},
"children": []
}
]
}
]
},
{
"data": {
"id": "cpd85t9xgw80",
"created": 1672207710972,
"text": "評価指標\n(Metrics)",
"font-family": "sans-serif",
"background": "transparent"
},
"children": [
{
"data": {
"id": "cphnxiir6u80",
"created": 1672658438233,
"text": "決定係数（R^2）\n(Coefficient of Determination)",
"note": "\n1 - \n(実測値 - 予測値)^2 の合計\n/ (実測値 - 実測値平均)^2 の合計\n\n= １ - 残差平方和 / 全平方和\n",
"hyperlink": "https://atmarkit.itmedia.co.jp/ait/articles/2108/25/news033.html",
"hyperlinkTitle": "@IT",
"font-family": "sans-serif"
},
"children": []
},
{
"data": {
"id": "cq7o5eggg1s0",
"created": 1675299615174,
"text": "残差平方和 RSS\n(Residual Sum of Squares)",
"font-family": "sans-serif",
"note": "残差^2 の合計\n= (実測値 - 予測値)^2 の合計\n\n二乗和誤差と同一視されることも多い\n",
"hyperlink": "https://atmarkit.itmedia.co.jp/ait/articles/2111/22/news011.html",
"hyperlinkTitle": "残差平方和（RSS：Residual Sum of Squares）／［損失関数］二乗和誤差（SSE：Sum of Squared Error）とは？"
},
"children": []
},
{
"data": {
"id": "cq7o6cbr5ag0",
"created": 1675299688900,
"text": "二乗和誤差 SSE\n(Sum of Squared Error)",
"font-family": "sans-serif",
"note": "誤差^2 の合計\n= (予測値 - 正解値)^2 の合計\n"
},
"children": []
}
]
},
{
"data": {
"id": "cqd02x806js0",
"created": 1675841074031,
"text": "自己回帰モデル AR\n(AutoRegressive Model)",
"font-family": "sans-serif"
},
"children": []
}
]
},
{
"data": {
"id": "cq08pqfr2ps0",
"created": 1674545151081,
"text": "荷重減衰\n(Weight Decay) ",
"font-family": "sans-serif"
},
"children": [
{
"data": {
"id": "cq08r1kbb2o0",
"created": 1674545253665,
"text": "正則化\n(Regularization)",
"font-family": "sans-serif",
"hyperlink": "https://aizine.ai/ridge-lasso-elasticnet/#toc9",
"hyperlinkTitle": "超入門！リッジ回帰・Lasso回帰・Elastic Netの基本と特徴をサクッと理解！"
},
"children": [
{
"data": {
"id": "cq8ko6ujzvs0",
"created": 1675391363048,
"text": "L0正則化",
"note": "0でないパラメータの個数\n",
"font-family": "sans-serif"
},
"children": []
},
{
"data": {
"id": "cq8kojfxkhs0",
"created": 1675391390462,
"text": "L1正則化／ラッソ回帰 Lasso",
"font-family": "sans-serif",
"note": "ラッソ回帰 Lasso\nL1ノルム＝重みの絶対値の合計\n※誤差のマンハッタン距離\n\n※L1(Lasso) / L2(Ridge) の覚え方\nhttps://zenn.dev/dlbsabu/articles/5300da50921070\n"
},
"children": []
},
{
"data": {
"id": "cq8kpct0k4g0",
"created": 1675391454380,
"text": "L2正則化／リッジ回帰 Ridge",
"font-family": "sans-serif",
"note": "リッジ回帰 Ridge\nL2ノルム＝重みの２乗平均\n※誤差のユークリッド距離\n"
},
"children": []
},
{
"data": {
"id": "cq8kpyol1ig0",
"created": 1675391502001,
"text": "ElasticNet",
"font-family": "sans-serif",
"note": "L1正則化・L2正則化の折衷案\n"
},
"children": []
}
]
}
]
},
{
"data": {
"id": "cpf67d39e6g0",
"created": 1672405310162,
"text": "異常検知 (Anomaly Detection)",
"font-family": "sans-serif",
"note": "●外れ値(outlier)検知\n\n●異常部位検出\n心電図で異常なパターンが含まれる部分とか\n\n●変化点検知\nある時刻を境に検索キーワード急上昇など\n\n異常検知入門と手法まとめ\nhttps://qiita.com/toucan/items/c3343de3cfa236df3bda\n",
"hyperlink": "https://www.albert2005.co.jp/knowledge/machine_learning/anomaly_detection_basics/anomaly_detection",
"hyperlinkTitle": "異常検知とは"
},
"children": []
}
]
},
{
"data": {
"id": "cpq94zqepx40",
"created": 1673530747212,
"text": "学習",
"font-family": "sans-serif",
"background": "#eaf1dd"
},
"children": [
{
"data": {
"id": "cqdo8k33fa80",
"created": 1675909222259,
"text": "損失関数 (Loss Function)",
"font-family": "sans-serif",
"note": "または\n誤差関数 (Error Function)\nコスト関数 (Cost Function)\n\n予測値と正解値の差を計算する関数\n"
},
"children": []
},
{
"data": {
"id": "cqfr2ah3pi80",
"created": 1676120314396,
"text": "評価関数 (Evaluation Function)",
"hyperlink": "https://atmarkit.itmedia.co.jp/ait/articles/2104/22/news022.html",
"hyperlinkTitle": "",
"font-family": "sans-serif",
"note": "機械学習モデルの性能を評価する関数\n"
},
"children": []
},
{
"data": {
"id": "cq01z7mwjjk0",
"created": 1674526146026,
"text": "重み更新に関わる単位",
"font-family": "sans-serif",
"background": "#eaf1dd"
},
"children": [
{
"data": {
"id": "cq01zu2fzdk0",
"created": 1674526194855,
"text": "バッチサイズ (Batch Size)",
"note": "一回のイテレーションに用いるサンプル数\n",
"font-family": "sans-serif"
},
"children": []
},
{
"data": {
"id": "cq01zm9l15s0",
"created": 1674526177873,
"text": "イテレーション (Iterations)",
"note": "重みが更新された回数\n1つのエポックを完了するために必要なバッチの数\n",
"font-family": "sans-serif"
},
"children": []
},
{
"data": {
"id": "cq01zr5b53s0",
"created": 1674526188498,
"text": "エポック (Epoch)",
"note": "訓練データを何回繰り返し学習したか\n(何回勾配を降下したか)\n",
"font-family": "sans-serif"
},
"children": []
}
]
},
{
"data": {
"id": "cpq95md7d600",
"created": 1673530796479,
"text": "オンライン学習\n(Online Learning)",
"font-family": "sans-serif"
},
"children": []
},
{
"data": {
"id": "cpq96h2dojk0",
"created": 1673530863305,
"text": "ミニバッチ学習\n(Mini-batch training)",
"font-family": "sans-serif"
},
"children": []
},
{
"data": {
"id": "cpf61atnkm80",
"created": 1672404835043,
"text": "アンサンブル学習\n(Ensemble Learning)",
"font-family": "sans-serif",
"hyperlink": "https://agency-star.co.jp/column/ensemble-learning",
"hyperlinkTitle": "アンサンブル学習とは？仕組みやアルゴリズムを解説！バギング、ブースティング、スタッキングの違いも紹介"
},
"children": [
{
"data": {
"id": "cpq85e4a8tc0",
"created": 1673527957416,
"text": "バギング\n(Bagging)",
"font-family": "sans-serif",
"hyperlink": "https://qiita.com/tjmnmn/items/3aed6fb85f75446f74ca",
"hyperlinkTitle": ""
},
"children": [
{
"data": {
"id": "cpp38mewmbs0",
"created": 1673412545059,
"text": "ランダムフォレスト\n(Random Forest)",
"font-family": "sans-serif",
"hyperlink": "https://aismiley.co.jp/ai_news/random-forests/",
"hyperlinkTitle": "ランダムフォレストとは？基本の仕組みから活用事例までまとめて解説"
},
"children": [
{
"data": {
"id": "cpsleajqcyg0",
"created": 1673768449262,
"text": "ブートストラップ\n(Bootstrap Sampling)",
"font-family": "sans-serif"
},
"children": []
}
]
}
]
},
{
"data": {
"id": "cpq85seku6o0",
"created": 1673527988513,
"text": "ブースティング\n(Boosting)",
"font-family": "sans-serif"
},
"children": [
{
"data": {
"id": "cpq868pvuio0",
"created": 1673528024025,
"text": "勾配ブースティング\n(Gradient Boosting)",
"font-family": "sans-serif"
},
"children": [
{
"data": {
"id": "cpz6jzu8uqo0",
"created": 1674437499186,
"text": "XGboost\n(eXtreme Gradient Boosting)",
"note": "2014年\n",
"hyperlink": "https://toukei-lab.com/xgboost",
"hyperlinkTitle": "",
"font-family": "sans-serif"
},
"children": [
{
"data": {
"id": "cqdmgvmc4ag0",
"created": 1675904232060,
"text": "Level-wise",
"note": "決定木の階層も併せて学習",
"font-family": "sans-serif"
},
"children": []
}
]
},
{
"data": {
"id": "cpz6jw4hq600",
"created": 1674437491099,
"text": "LightGBM",
"note": "2016年\n\n高速化（精度高いとは限らない）\n\nジニ不純度を計算して、分類が不十分なものを優先的に分岐を増やす\n",
"hyperlink": "https://toukei-lab.com/light-gbm",
"hyperlinkTitle": "",
"font-family": "sans-serif"
},
"children": [
{
"data": {
"id": "cqdmgisbhi80",
"created": 1675904204124,
"text": "Leaf-wise",
"note": "決定木の葉ごとに学習\n※過学習しやすい\n",
"font-family": "sans-serif"
},
"children": []
}
]
},
{
"data": {
"id": "cpz6kmbvy8w0",
"created": 1674437548142,
"text": "CatBoost\n(Category Boosting)",
"hyperlink": "https://toukei-lab.com/catboost",
"hyperlinkTitle": "",
"font-family": "sans-serif"
},
"children": []
}
]
},
{
"data": {
"id": "cpq8606nyso0",
"created": 1673528005449,
"text": "AdaBoost\n(Adaptive Boosting)",
"font-family": "sans-serif",
"hyperlink": "https://aizine.ai/adaboost-0128/",
"hyperlinkTitle": "機械学習を行うなら必須！「AdaBoost（アダブースト）」とは"
},
"children": []
}
]
},
{
"data": {
"id": "cptmvswccv40",
"created": 1673874202464,
"text": "スタッキング\n(Stacking）",
"font-family": "sans-serif",
"hyperlink": "https://www.codexa.net/what-is-ensemble-learning/",
"hyperlinkTitle": "機械学習上級者は皆使ってる？！アンサンブル学習の仕組みと3つの種類について解説します"
},
"children": []
}
]
}
]
},
{
"data": {
"id": "cpd7sixjjs80",
"created": 1672206669721,
"text": "モデルの評価",
"font-family": "sans-serif",
"background": "#eaf1dd"
},
"children": [
{
"data": {
"id": "cpo83hc3m8o0",
"created": 1673324687777,
"text": "data leakage, target leakage",
"note": "予測時には入手できない筈のデータが学習に含まれてしまう\n",
"font-family": "sans-serif"
},
"children": []
},
{
"data": {
"id": "cpf2dca30io0",
"created": 1672394494143,
"text": "交差検証\n(cross validation)",
"font-family": "sans-serif"
},
"children": [
{
"data": {
"id": "cpoz5ujffr40",
"created": 1673401043214,
"text": "ホールドアウト (hold-out)",
"font-family": "sans-serif"
},
"children": []
},
{
"data": {
"id": "cpoz74ydhi00",
"created": 1673401144250,
"text": "k-分割交差検証\n(K-fold Cross-validation)",
"font-family": "sans-serif"
},
"children": []
}
]
},
{
"data": {
"id": "cptn2o0x4yo0",
"created": 1673874740406,
"text": "訓練誤差\n(training error)",
"font-family": "sans-serif"
},
"children": []
},
{
"data": {
"id": "cpf6f05knc00",
"created": 1672405908917,
"text": "汎化誤差\n(generalization error)",
"font-family": "sans-serif"
},
"children": [
{
"data": {
"id": "cq0xfb2if400",
"created": 1674614861734,
"text": "バイアス (Bias)",
"font-family": "sans-serif"
},
"children": [
{
"data": {
"id": "cpq84193jfk0",
"created": 1673527851044,
"text": "未学習 (Underfitting)",
"font-family": "sans-serif"
},
"children": []