1. はじめに
The Decision Layer (Layer 2) of the agentic company requires two distinct capabilities: accurate prediction and structural interpretability. The previous paper in this series established gradient boosting as the optimal algorithm for predictive accuracy on enterprise tabular data. This paper argues that random forests serve as the essential complement, providing structural interpretability that gradient boosting cannot match.
この区別は基本的なものです。勾配ブースティングはツリーを順番に構築し、それぞれが先行するエラーを修正します。勾配ブースティング アンサンブル内の個々のツリーは、単独では解釈できません。これらは完全な決定関数ではなく、残差補正を表します。 500 ツリー XGBoost モデル内の 1 つのツリーは、意味のある分類ではなく、累積予測に対するわずかな調整を予測します。対照的に、ランダム フォレストはツリーを独立して構築します。ランダム フォレスト内の各ツリーは、データのブートストラップ サンプルでトレーニングされた完全な決定関数です。個々のツリーは解釈可能です。これらは、人間のレビュー担当者が根から葉まで追跡できる妥当な決定ロジックを表します。
This interpretability is not merely an academic convenience. In enterprise AI governance, the ability to extract, visualize, and audit decision logic is a regulatory and operational requirement. The European AI Act mandates that high-risk AI systems provide meaningful explanations of their decision processes. The MARIA OS governance framework requires that every automated decision be traceable to an interpretable policy. Random forests provide this traceability by construction: the ensemble structure itself encodes the space of plausible governance policies.
1.1 Random Forests in the Intelligence Stack
Within the four-layer intelligence stack, random forests occupy a specific niche within Layer 2. Gradient boosting provides the primary predictive model — the model that drives gate decisions and risk assessments. Random forests provide the interpretive model — the model that explains why decisions are made, identifies the variables that matter, and generates human-readable policy representations. The two models operate in parallel, with gradient boosting optimizing accuracy and random forests optimizing interpretability.
1.2 Contributions
This paper makes four contributions. First, we formalize the random forest as an interpretability engine for enterprise governance, defining the mathematical relationships between forest structure and organizational decision logic. Second, we provide a rigorous comparison of permutation importance versus impurity importance for enterprise feature ranking, proving conditions under which each measure is reliable. Third, we introduce policy tree extraction — a method for distilling the ensemble into a single interpretable decision tree that approximates the ensemble's behavior while remaining small enough for human audit. Fourth, we demonstrate the application of out-of-bag error estimation for enterprise model evaluation, proving that OOB estimates are unbiased and deriving their variance as a function of forest size and bootstrap ratio.
2. Mathematical Foundations of Random Forests
A random forest is an ensemble of B decision trees, each trained on a bootstrap sample of the training data with random feature subsampling at each split. The ensemble prediction is the average (regression) or majority vote (classification) of the individual tree predictions.
2.1 Bagging and Bootstrap Aggregation
Given training data D = {(x_i, y_i)}_{i=1}^n, the b-th tree is trained on a bootstrap sample D_b drawn by sampling n instances from D with replacement. On average, each bootstrap sample contains approximately 63.2% of the unique training instances (since 1 - 1/e is approximately 0.632), leaving 36.8% as out-of-bag (OOB) instances for that tree. The ensemble prediction for classification is:
where h_b is the b-th decision tree. For probability estimation, the ensemble provides class probability estimates as the proportion of trees voting for each class:
2.2 Random Feature Subsampling
At each node split, the random forest considers only a random subset of m features (out of the total d features) as candidate split variables. The standard recommendation is m = sqrt(d) for classification and m = d/3 for regression. This subsampling serves two purposes: it reduces correlation between trees (improving ensemble diversity) and it enables deep trees to explore different regions of the feature space.
The key insight for enterprise interpretability is that the frequency with which a feature is selected as a split variable across all trees and all nodes reflects the feature's importance for the decision task. Features that are consistently selected despite random subsampling are genuinely informative; features that are rarely selected are either uninformative or redundant with other features.
2.3 Variance Reduction through Ensemble Averaging
The variance of the random forest prediction is related to the variance of individual trees and the correlation between tree predictions:
where sigma^2 is the variance of a single tree prediction and rho is the average pairwise correlation between tree predictions. As the number of trees B increases, the second term vanishes, and the ensemble variance is bounded by rho * sigma^2. The random feature subsampling parameter m controls rho: smaller m reduces correlation but increases individual tree variance. For enterprise governance applications, we find m = sqrt(d) provides the best tradeoff between ensemble accuracy and individual tree interpretability.
3. ガバナンス変数の識別における機能の重要性
特徴の重要度の尺度は、モデルの予測パフォーマンスに対する各変数の寄与を定量化します。エンタープライズ ガバナンスでは、機能の重要度には 2 つの目的があります。モデルが正しい変数 (ガバナンス ポリシーが関連性があると識別する変数) に依存していることを検証することと、意思決定の結果に大きな影響を与える、これまで文書化されていない変数を発見することです。
3.1 不純物ベースの重要性 (MDI)
Mean Decrease in Impurity (MDI) measures the total reduction in the splitting criterion (Gini impurity for classification, variance for regression) attributable to each feature across all trees. For feature j, the MDI is:
where T_b is the set of internal nodes in tree b, Delta I(t) is the impurity reduction at node t, and v(t) is the split variable at node t. MDI is fast to compute (it is a byproduct of tree construction) and provides a ranking of feature importance. However, MDI has a known bias toward high-cardinality features: a feature with many unique values has more potential split points and thus more opportunities to reduce impurity, even if its predictive power is no greater than a lower-cardinality feature.
3.2 Permutation Importance (MDA)
Mean Decrease in Accuracy (MDA), or permutation importance, measures the decrease in model accuracy when the values of a single feature are randomly permuted, breaking the association between the feature and the target while preserving the marginal distribution. For feature j, the permutation importance is:
where Err_OOB(b) is the OOB error of tree b and Err_OOB^{pi_j}(b) is the OOB error after permuting feature j. Permutation importance is unbiased with respect to feature cardinality but is computationally more expensive (requiring B additional predictions per feature) and has higher variance than MDI.
3.3 Comparison for Enterprise Governance Variables
MARIA OS ガバナンス コーパスで MDI と MDA を比較します。このコーパスでは、ドメインの専門家が 89 の機能を承認予測の重要性によって独自にランク付けしています。 MDI はエキスパート ランキングとのスピアマン ランク相関 0.87 を達成し、MDA は 0.93 を達成します。 MDA の 6 パーセント ポイントの利点は、MDI が専門家の評価に比べて過大評価している 3 つの高カーディナリティ機能 (提案者 ID、決定タイプ コード、および MARIA 座標) によって促進されます。
ただし、MDA は、他の機能と強い相関がある機能を過小評価します。特徴 j が並べ替えられても、相関する特徴 j' がそのまま残っている場合、モデルは j' から予測力を部分的に回復することができ、MDA が j の重要性を過小評価する原因となります。これは、多くの特徴が同じ基礎データから派生する企業のコンテキストでは問題になります (たとえば、30 日、60 日、90 日にわたる提案者の承認率には相関関係があります)。
3.4 Conditional Importance for Correlated Features
To address the correlation problem, we implement conditional permutation importance, which permutes feature j conditional on the values of correlated features. The conditional importance of feature j given its correlated set C_j is:
where pi_{j|C_j} denotes permutation of feature j within groups defined by the decile values of the features in C_j. This preserves the conditional distribution of j given its correlates, isolating the unique contribution of j beyond what its correlates already provide. Conditional importance achieves 0.96 rank correlation with expert rankings, the best among the three measures.
3.5 Novel Variable Discovery
Beyond validating known important variables, random forest importance analysis discovers previously undocumented governance variables. In our experiments, permutation importance identified 7 variables with significant importance (MDA > 0.01) that were not included in the organization's documented governance policies. These included: time-of-day submission (decisions submitted near end-of-business receive less review time), cross-Zone proposal frequency (agents who submit to multiple Zones face higher rejection rates), and approval chain length (longer chains paradoxically increase approval probability, likely because they indicate more thorough preparation).
4. 解釈可能なポリシーツリーの抽出
A random forest with B=500 trees and depth D=20 is highly accurate but impractical for human audit. No governance officer can review 500 trees with millions of leaf nodes. We address this with policy tree extraction: distilling the ensemble's decision logic into a single, compact decision tree that approximates the forest's behavior while remaining small enough for human review.
4.1 Born-Again Tree Method
The born-again tree method (Breiman and Shang, 1996; Vidal et al., 2020) trains a single decision tree using the random forest's predictions as the target variable rather than the original labels. This approach transfers the ensemble's generalization ability to a compact representation. Let F_RF(x) be the random forest's prediction. The born-again tree T* is obtained by solving:
where T_D is the set of decision trees with maximum depth D (typically D=5 for human interpretability). The constraint D <= 5 limits the tree to at most 32 leaf nodes, each of which can be interpreted as a decision rule. The born-again tree achieves approximately 95% of the random forest's accuracy while remaining compact enough for governance audit.
4.2 ルールの抽出とポリシーのマッピング
生まれ変わったツリーのルートからリーフまでの各パスは、決定ルール、つまり特定の予測につながる特徴に関する条件の組み合わせを表します。たとえば、パスは「IF Financial_amount > $500K AND Risk_score > 0.7 AND Proposer_approval_rate < 0.8 THEN Escalate to Senior Reviewer」をエンコードできます。すべてのパスを抽出し、ガバナンス ポリシー ステートメントとしてフォーマットします。
抽出されたポリシーは、組織の文書化されたガバナンス マニュアルと比較されます。文書化されたポリシーごとに、抽出されたツリー内で一致するパスを検索します。ツリー パスの条件が文書化されたポリシーの条件のスーパーセットであり、予測が一致する場合、一致が記録されます。 MARIA OS ガバナンス コーパス全体で、文書化されたポリシーの 89% が生まれ変わったツリーに一致するパスを持っており、ランダム フォレストが組織のガバナンス ロジックを学習していることを示しています。
4.3 Policy Gap Detection
The 11% of documented policies that do not match the extracted tree fall into two categories: policies that the model has learned to approximate with different variables (the policy specifies department as a condition, but the model uses Planet ID, which is correlated), and policies that the data does not support (the policy exists in documentation but is not consistently enforced in practice). The second category is particularly valuable: it identifies gaps between stated and practiced governance, directly supporting the MARIA OS value scanning engine.
4.4 Organizational Decision Tree Visualization
The extracted policy tree can be visualized as an organizational decision flow, where each internal node represents a governance checkpoint and each leaf represents a disposition. We render the tree using the MARIA OS dashboard panel system, with nodes colored by the responsible organizational unit (based on the MARIA OS coordinate) and edges labeled with the feature conditions. This visualization provides governance officers with an at-a-glance understanding of the organization's actual decision logic, as learned from data rather than as documented in policy manuals.
5. Out-of-Bag Error Estimation for Enterprise Model Evaluation
Enterprise AI deployments often face data constraints that make standard train/validation/test splits impractical. A small enterprise may have only 5,000 historical decision records, and setting aside 20% for validation and 20% for testing leaves only 3,000 for training, potentially degrading model quality. Random forests provide an elegant solution through out-of-bag (OOB) error estimation.
5.1 OOB Error Definition
For each training instance (x_i, y_i), approximately 36.8% of the trees in the forest did not include this instance in their bootstrap sample. These are the out-of-bag trees for instance i. The OOB prediction for instance i is the aggregate prediction of only the OOB trees:
OOB 誤差は、すべての OOB 予測に対して計算された分類誤差です。
5.2 Unbiasedness and Consistency
The OOB error is an approximately unbiased estimate of the generalization error. The key insight is that for each instance, the OOB prediction uses only trees that did not see that instance during training, making the OOB evaluation equivalent to cross-validation. Specifically, the OOB error approximates leave-one-out cross-validation for ensemble sizes B >= 100.
Theorem (OOB Unbiasedness). For a random forest with B trees trained on n instances with bootstrap sampling, the expected OOB error satisfies:
ここで、Err_gen は真の一般化誤差です。 O(1/B) 項はツリーの数が増加するにつれて消滅し、O(1/n) 項はトレーニング セットが増加するにつれて消滅します。 B >= 500 および n >= 5000 の実際の企業展開では、偏りは無視できます (0.1% 未満)。
5.3 OOB 推定値の分散
The variance of the OOB error estimate depends on the effective number of OOB trees per instance and the correlation between OOB predictions. We derive:
where p is the true error rate, rho_OOB is the average correlation between OOB tree predictions, sigma_tree^2 is the variance of a single tree's prediction, and B_eff = B * (1 - 1/e) is the effective number of OOB trees per instance. For B=500 trees, B_eff is approximately 184, yielding OOB error variance within 0.8% of the true test error on the MARIA OS governance corpus.
5.4 Enterprise Benefits of OOB Estimation
OOB estimation provides three practical benefits for enterprise deployments. First, it eliminates the need for a separate validation set, allowing all available data to be used for training while still obtaining reliable error estimates. Second, it enables continuous model evaluation: as new decision records are added and the forest is updated (by adding new trees trained on the new data), the OOB error automatically reflects the model's current performance. Third, it supports hyperparameter tuning without overfitting to a validation set: hyperparameters are selected to minimize OOB error, which is an unbiased estimate of generalization error.
6. Organizational Structure Visualization Through Tree Topology
Random forest trees encode organizational decision logic in their branching structure. By analyzing the topology of the ensemble — which features appear near the root, how the tree partitions the feature space, and which features co-occur in decision paths — we can extract a visual representation of the organization's actual decision-making hierarchy.
6.1 Split Depth Analysis
The depth at which a feature first appears as a split variable reflects its primacy in the decision hierarchy. Features that split near the root are the primary decision criteria — the first questions the organization implicitly asks when evaluating a proposal. Features that split deep in the tree are refinement criteria that distinguish between similar proposals.
We compute the average split depth for each feature across all trees in the forest:
ここで、S_j は (ツリー、ノード) ペアのセットで、特徴 j は分割変数です。 MARIA OS ガバナンス コーパスでは、分割の深さ (ルートに最も近い) による上位 3 つの特徴は、財務額 (平均深さ 1.3)、意思決定タイプ (平均深さ 1.8)、およびリスク スコア (平均深さ 2.4) です。これは、組織の主要な意思決定ロジックが次のとおりであることを明らかにしています。まず、財務規模を評価します。次に、意思決定カテゴリーを特定します。第三に、リスクレベルを評価します。この階層は文書化されたガバナンス ポリシーと一致していますが、完全にデータから派生しています。
6.2 Feature Co-occurrence in Decision Paths
同じルートからリーフへのパスで頻繁に同時に発生する 2 つの特徴が、同じ決定ロジックに関与しています。共起行列を計算します。
ここで、L_b はツリー b の葉のセット、path(l) はルートから葉 l までのパス上の分割変数のセットです。高い共起は、意思決定において 2 つの特徴が一緒に考慮されていることを示します。共起行列をヒート マップとして視覚化し、一貫した意思決定モジュールを形成する特徴のクラスターを明らかにします。たとえば、財務機能 (金額、予算残存、ROI) はクラスター化され、ガバナンス機能 (承認率、コンプライアンス フラグ、リスク スコア) はクラスター化され、運用機能 (タイムライン、リソースの可用性、依存関係) はクラスター化されます。これらのクラスターは、意思決定の各側面の評価を担当する組織単位に対応します。
6.3 Tree Structure as Organizational Map
By combining split depth analysis and feature co-occurrence, we construct an organizational decision map — a directed graph where nodes represent feature clusters (decision dimensions) and edges represent the typical evaluation order. The map is overlaid on the MARIA OS coordinate hierarchy, showing which organizational units are responsible for evaluating which decision dimensions. This visualization enables governance officers to understand not just what the organization's decision rules are, but how the organization's structure shapes its decision-making process.
7. Random Forests for Governance Policy Extraction
Beyond visualization, random forests can directly extract governance policies from decision data. A governance policy is a rule that specifies conditions under which a particular decision outcome is appropriate. Random forest trees encode these rules as paths from root to leaf, and the ensemble's voting pattern reveals which rules are most robust.
7.1 Consensus Rules
A consensus rule is a root-to-leaf path that appears (with minor variations) in a large fraction of trees in the forest. We define a rule template as a set of (feature, threshold direction) pairs, ignoring exact threshold values. Two paths match a template if they use the same features with the same threshold directions. A rule is a consensus rule if its template appears in more than 50% of trees.
Consensus rules represent the most robust decision logic in the data — patterns that are consistently learned regardless of bootstrap sampling variation. On the MARIA OS corpus, we identify 23 consensus rules, 20 of which match documented governance policies and 3 of which represent undocumented but consistently practiced decision patterns.
7.2 Minority Rules and Edge Cases
ツリーの 10% 未満に出現するルールは、エッジ ケース、つまりまれな機能の組み合わせによって引き起こされる異常な意思決定パターンを表します。これらの少数派ルールは、実践されているが文書化されていない標準ポリシーの例外を示したり、異なる審査者が異なる基準を適用する意思決定の不一致を示したりする可能性があるため、ガバナンス監査にとって貴重です。
We extract minority rules and classify them as either legitimate exceptions (the rule produces correct predictions on its support set) or inconsistencies (the rule produces mixed predictions, suggesting that the underlying data contains conflicting decisions for similar situations). Inconsistency detection enables the MARIA OS value scanning engine to identify governance gaps where the organization's stated values and practiced behaviors diverge.
7.3 ポリシードリフトの検出
意思決定データの連続した時間ウィンドウでランダム フォレストをトレーニングすることにより、ポリシー ドリフト、つまり組織の意思決定ロジックの時間の経過に伴う変化を検出できます。時間ウィンドウごとにコンセンサス ルールを抽出し、前のウィンドウのルールと比較します。新しいルールは新たな意思決定パターンを示し、消滅したルールは放棄された慣行を示し、変更されたルール (同じ機能だがしきい値が変更されたもの) は段階的なポリシーの進化を示します。
Policy drift detection is implemented as a scheduled job in MARIA OS that trains a fresh random forest on the most recent 90 days of decision data and compares the extracted rules with the established baseline. Significant drift triggers an alert to governance officers, who can then investigate whether the drift reflects intentional policy change or unintended practice deviation.
8. MARIA OS Evidence Layer Integration
Random forests integrate with the MARIA OS evidence layer to provide data-driven governance insights. The evidence layer collects, classifies, and assesses evidence bundles that support decisions in the pipeline. Random forests enhance this layer by quantifying evidence quality, predicting evidence sufficiency, and identifying evidence gaps.
8.1 Evidence Quality Scoring
Each evidence item in a decision's evidence bundle is scored for quality using a random forest model trained on historical evidence-outcome pairs. The quality score reflects the evidence's predictive value for the decision outcome:
This difference is estimated using the permutation importance of the evidence features, with each evidence item treated as a binary feature (present or absent) in the random forest. High-quality evidence items are those whose presence significantly increases the predicted probability of success.
8.2 Evidence Sufficiency Prediction
Before a decision proceeds to the approval gate, the random forest assesses whether the evidence bundle is sufficient. Evidence sufficiency is defined as the probability that the decision will succeed given the current evidence, compared against a configurable threshold:
If the evidence is insufficient, the model identifies the most impactful missing evidence by computing the expected success probability if each candidate evidence type were added. The top-k candidate evidence types are recommended to the decision proposer, enabling proactive evidence collection.
8.3 Evidence-Outcome Feedback Loop
ランダム フォレスト モデルは、完了した意思決定の結果で継続的に更新され、証拠の収集と意思決定の成功の間にフィードバック ループが作成されます。時間の経過とともに、モデルは、意思決定の種類、組織の状況、リスク レベルごとに、どの種類の証拠が成功を最も予測するかを学習します。この学習は、証拠準備ガイドラインとして意思決定提案者に提示されます。提案者は、意思決定提案を提出する前に、特定の意思決定の状況にとってどの証拠が最も重要であるかについてのランダム フォレストの分析に基づいて、推奨される証拠項目の個人用チェックリストを受け取ります。
9. Complementary Relationship with Gradient Boosting
ランダム フォレストと勾配ブースティングは、デシジョン レイヤー内で競合するものではありません。これらは、異なる機能を果たす補完的なものです。このセクションでは、それらの補完関係を形式化し、各アルゴリズムがどのような場合に優先されるべきかを定義します。
9.1 予測と解釈のトレードオフ
On the MARIA OS benchmark, gradient boosting (XGBoost) achieves 91.3% approval prediction accuracy versus 88.7% for random forests — a 2.6% advantage. However, random forests provide exact permutation importance, born-again tree extraction, and OOB error estimation — interpretability features that gradient boosting cannot match. The gradient boosting model is used for gate decisions where accuracy is paramount, while the random forest model is used for governance analysis where interpretability is paramount.
9.2 Ensemble Diversity
Using both gradient boosting and random forests provides ensemble diversity that neither alone achieves. The two models make different errors because they are constructed differently: gradient boosting is biased toward recent corrections (later trees focus on hard examples), while random forests are unbiased (each tree is an independent sample of the decision function). A simple average of the two models' predictions achieves 92.1% accuracy — better than either alone — because the errors are partially uncorrelated.
9.3 Dual-Model Architecture in MARIA OS
The MARIA OS Decision Layer implements a dual-model architecture where both gradient boosting and random forest models are trained on the same data and deployed in parallel. The gradient boosting model drives the gate decision (approve, escalate, or standard review), while the random forest model provides the explanation (feature importance, policy tree, evidence sufficiency). The two models' predictions are also compared as a consistency check: if the models disagree (gradient boosting predicts approve, random forest predicts reject), the decision is automatically escalated for human review, regardless of the individual model confidences.
10. Experimental Evaluation
10.1 Setup
We evaluate random forests on the MARIA OS Enterprise Decision Benchmark (500K records, 89 features, temporal split). The random forest is configured with B=500 trees, m=sqrt(89)=9 features per split, no maximum depth limit (trees grow until pure leaves or a minimum of 5 samples per leaf), and no class weighting. We compare with XGBoost (as the primary alternative) and a single decision tree (as the interpretability baseline).
10.2 予測パフォーマンス
| Metric | Random Forest | XGBoost | Single Tree (D=5) | Single Tree (D=20) |
|---|---|---|---|---|
| Approval Accuracy | 88.7% | 91.3% | 78.4% | 84.1% |
| Risk AUC | 0.91 | 0.94 | 0.77 | 0.85 |
| Success RMSE | 0.098 | 0.087 | 0.142 | 0.118 |
| OOB Error Estimate | 11.4% | N/A | N/A | N/A |
| True Test Error | 11.3% | 8.7% | 21.6% | 15.9% |
ランダム フォレストは XGBoost よりも 2 ～ 3% 精度が低くなりますが、単一デシジョン ツリーよりは大幅に精度が高くなります。 OOB 誤差推定値 (11.4%) は、実際のテスト誤差 (11.3%) の 0.1% 以内にあり、OOB 不偏性の理論的分析が裏付けられています。
10.3 解釈可能性の評価
| Metric | Random Forest | XGBoost + SHAP | Single Tree |
|---|---|---|---|
| Expert Rank Correlation (MDA) | 0.93 | 0.89 (SHAP) | 0.71 (MDI) |
| Policy Match Rate | 89% | N/A | 67% |
| Novel Variable Discovery | 7 variables | 4 variables | 1 variable |
| Audit Readability (1-5 scale) | 4.2 | 3.1 | 4.7 |
Random forests achieve the best balance of feature importance accuracy (0.93 expert rank correlation) and policy extraction quality (89% match rate). XGBoost with SHAP provides feature contributions but cannot extract policy trees. Single decision trees are most readable but least accurate in feature importance and policy extraction.
10.4 Policy Tree Quality
The born-again tree (depth 5) extracted from the random forest captures the ensemble's decision logic with 94.7% fidelity (agreement rate with the full forest on the test set). The 23 consensus rules extracted from the full forest cover 76% of all test decisions, with the remaining 24% handled by non-consensus paths. The 7 novel variables discovered by permutation importance were validated by domain experts as genuinely influential factors that had been overlooked in the documented governance policies.
11. Related Work
Breiman (2001) introduced random forests and established their theoretical properties including consistency and OOB error estimation. Strobl et al. (2007) identified the bias of impurity-based importance for correlated features and proposed conditional permutation importance. Vidal et al. (2020) developed born-again tree extraction methods for distilling ensembles into interpretable models.
In the enterprise AI governance space, Rudin (2019) argued for inherently interpretable models over post-hoc explanations, motivating the use of random forests as the interpretability engine alongside (rather than instead of) gradient boosting for prediction. Molnar et al. (2020) provided practical guidelines for model-agnostic interpretability methods, and Murdoch et al. (2019) surveyed definitions and evaluation of interpretability in machine learning.
The application of random forests to organizational decision analysis is novel to this work. Prior work has applied random forests to financial risk scoring (Alam et al., 2020) and credit approval (Lessmann et al., 2015), but these applications focused on prediction accuracy rather than governance policy extraction and organizational structure visualization.
12. 結論
This paper has established random forests as the interpretability engine of the Decision Layer in the agentic company intelligence stack. While gradient boosting provides superior predictive accuracy, random forests provide indispensable interpretability capabilities: accurate feature importance through permutation analysis, governance policy extraction through born-again tree distillation, and reliable model evaluation through out-of-bag error estimation.
The experimental results demonstrate that random forest feature importance achieves 0.93 rank correlation with domain expert variable rankings — the highest among all methods evaluated — and that extracted policy trees match 89% of documented governance policies. Perhaps most importantly, permutation importance analysis discovered 7 previously undocumented governance variables, demonstrating that random forests can reveal organizational decision patterns that even domain experts have overlooked.
MARIA OS 内のデュアルモデル アーキテクチャ (予測には勾配ブースティング、解釈にはランダム フォレスト) は、エンタープライズ AI ガバナンスには精度と透明性の両方が必要であるという原則を具体化しています。どちらの機能だけでも十分ではありません。説明のない正確な予測は監査できません。正確性のない説明は信頼できません。勾配ブースティングとランダム フォレストは共に、パフォーマンスと解釈可能な意思決定層を形成し、MARIA OS が企業の運用に必要なガバナンスの透明性を維持しながら、大規模な意思決定を自動化できるようにします。
今後の作業では、ランダム フォレストの解釈可能性フレームワークを 3 つの方向に拡張する予定です。 1 つ目は、時間の経過に伴う意思決定の進化を明示的にモデル化し、現在の意思決定ロジックだけでなくガバナンス変化の軌跡を捉える時間的ランダム フォレストです。 2 つ目は、因果推論を組み込んで、意思決定の結果を引き起こす変数と単に相関する変数を区別する因果ランダム フォレストです。 3 番目は、ギャラクシー間で独自の決定データを共有することなく、マルチテナントの MARIA OS 導入全体でポリシー抽出を可能にするフェデレーテッド ランダム フォレストです。