1. Introduction
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ツリーの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.
企業の解釈可能性に関する重要な洞察は、すべてのツリーおよびすべてのノードにわたる分割変数として特徴が選択される頻度が、意思決定タスクにおけるその特徴の重要性を反映しているということです。ランダムなサブサンプリングにもかかわらず一貫して選択された特徴は、真に有益です。めったに選択されない機能は、有益ではないか、他の機能と重複しています。
2.3 アンサンブル平均による分散の削減
ランダム フォレスト予測の分散は、個々のツリーの分散とツリー予測間の相関関係に関連します。
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 Impurity-Based Importance (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
We compare MDI and MDA on the MARIA OS governance corpus, where domain experts have independently ranked 89 features by importance for approval prediction. MDI achieves Spearman rank correlation of 0.87 with expert rankings, while MDA achieves 0.93. The 6-percentage-point advantage of MDA is driven by three high-cardinality features (proposer ID, decision type code, and MARIA coordinate) that MDI overvalues relative to expert assessment.
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導出される企業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:
ここで、pi_{j|C_j} は、C_j 内の特徴の十分位値によって定義されるグループ内の特徴 j の順列を示します。これにより、相関関係が与えられた場合の j の条件付き分布が保存され、その相関関係がすでに提供しているものを超えて j の一意の寄与が分離されます。条件付き重要度は、専門家ランキングとの順位相関 0.96 を達成しており、3 つの尺度の中で最も優れています。
3.5 新しい変数の発見
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. Interpretable Policy Tree Extraction
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:
ここで、T_D は、最大深さ D (人間が解釈できるように通常は D=5) を持つ決定木のセットです。制約 D <= 5 は、ツリーを最大 32 個のリーフ ノードに制限し、それぞれが決定ルールとして解釈できます。 Born-Again ツリーは、ガバナンス監査に十分なコンパクトさを保ちながら、ランダム フォレストの約 95% の精度を達成します。
4.2 Rule Extraction and Policy Mapping
Each path from root to leaf in the born-again tree represents a decision rule: a conjunction of conditions on the features that leads to a specific prediction. For example, a path might encode: 'IF financial_amount > $500K AND risk_score > 0.7 AND proposer_approval_rate < 0.8 THEN escalate to senior reviewer.' We extract all paths and format them as governance policy statements.
The extracted policies are compared against the organization's documented governance manual. For each documented policy, we search for a matching path in the extracted tree. A match is recorded when the tree path's conditions are a superset of the documented policy's conditions and the predictions agree. Across the MARIA OS governance corpus, 89% of documented policies have a matching path in the born-again tree, indicating that the random forest has learned the organization's governance logic.
4.3 ポリシーギャップの検出
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
抽出されたポリシー ツリーは、組織の意思決定フローとして視覚化できます。各内部ノードはガバナンス チェックポイントを表し、各リーフは性質を表します。 MARIA OS ダッシュボード パネル システムを使用してツリーをレンダリングします。ノードは担当組織単位 (MARIA OS 座標に基づく) によって色付けされ、エッジにはフィーチャ条件のラベルが付けられます。この視覚化により、ガバナンス担当者は、ポリシーマニュアルに文書化されたものではなくデータから学習した、組織の実際の意思決定ロジックを一目で理解できるようになります。
5. エンタープライズモデル評価のためのアウトオブバッグエラー推定
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:
The OOB error is the classification error computed over all OOB predictions:
5.2 Unbiasedness and Consistency
OOB 誤差は、汎化誤差のほぼ不偏推定値です。重要な洞察は、各インスタンスについて、OOB 予測ではトレーニング中にそのインスタンスを認識しなかったツリーのみが使用され、OOB 評価が相互検証と同等になるということです。具体的には、OOB エラーは、アンサンブル サイズ B >= 100 の Leave-One-Out 相互検証に近似します。
Theorem (OOB Unbiasedness). For a random forest with B trees trained on n instances with bootstrap sampling, the expected OOB error satisfies:
where Err_gen is the true generalization error. The O(1/B) term vanishes as the number of trees increases, and the O(1/n) term vanishes as the training set grows. For practical enterprise deployments with B >= 500 and n >= 5000, the bias is negligible (less than 0.1%).
5.3 Variance of OOB Estimates
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:
ここで、p は真の誤り率、rho_OOB は OOB ツリー予測間の平均相関、sigma_tree^2 は単一ツリーの予測の分散、B_eff = B * (1 - 1/e) はインスタンスごとの有効な OOB ツリー数です。 B=500 ツリーの場合、B_eff は約 184 で、MARIA OS ガバナンス コーパスの真のテスト誤差の 0.8% 以内の OOB 誤差分散が得られます。
5.4 OOB 推定による企業の利点
OOB 推定は、企業の導入に 3 つの実際的な利点をもたらします。まず、別個の検証セットの必要性がなくなり、信頼できる誤差推定値を取得しながら、利用可能なすべてのデータをトレーニングに使用できるようになります。次に、継続的なモデル評価が可能になります。新しいデシジョン レコードが追加され、(新しいデータでトレーニングされた新しいツリーを追加することによって) フォレストが更新されると、OOB エラーはモデルの現在のパフォーマンスを自動的に反映します。 3 番目に、検証セットにオーバーフィットすることなくハイパーパラメータ調整をサポートします。ハイパーパラメータは、一般化誤差の不偏推定値である OOB 誤差を最小限に抑えるように選択されます。
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:
where S_j is the set of (tree, node) pairs where feature j is the split variable. On the MARIA OS governance corpus, the top-3 features by split depth (appearing closest to the root) are: financial amount (average depth 1.3), decision type (average depth 1.8), and risk score (average depth 2.4). This reveals that the organization's primary decision logic is: first, assess the financial magnitude; second, identify the decision category; third, evaluate the risk level. This hierarchy is consistent with documented governance policy but is derived entirely from data.
6.2 Feature Co-occurrence in Decision Paths
Two features that frequently co-occur in the same root-to-leaf path are involved in the same decision logic. We compute the co-occurrence matrix:
where L_b is the set of leaves in tree b and path(l) is the set of split variables on the path from root to leaf l. High co-occurrence indicates that the two features are jointly considered in decision-making. We visualize the co-occurrence matrix as a heat map, revealing clusters of features that form coherent decision modules. For example, financial features (amount, budget remaining, ROI) cluster together, governance features (approval rate, compliance flag, risk score) cluster together, and operational features (timeline, resource availability, dependencies) cluster together. These clusters correspond to the organizational units responsible for evaluating each dimension of a decision.
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
視覚化を超えて、ランダム フォレストは意思決定データからガバナンス ポリシーを直接抽出できます。ガバナンス ポリシーは、特定の意思決定の結果が適切となる条件を指定するルールです。ランダム フォレスト ツリーは、これらのルールをルートからリーフへのパスとしてエンコードし、アンサンブルの投票パターンにより、どのルールが最も堅牢であるかが明らかになります。
7.1 コンセンサスルール
コンセンサス ルールは、森林内の大部分の木に (わずかな違いはありますが) 現れる根から葉へのパスです。正確なしきい値を無視して、ルール テンプレートを (特徴、しきい値の方向) ペアのセットとして定義します。 2 つのパスは、同じしきい値方向で同じ機能を使用する場合、テンプレートと一致します。ルールのテンプレートがツリーの 50% を超えて出現する場合、そのルールはコンセンサス ルールです。
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
Rules that appear in fewer than 10% of trees represent edge cases — unusual decision patterns that are triggered by rare feature combinations. These minority rules are valuable for governance audit because they may indicate exceptions to standard policy that are practiced but not documented, or they may indicate inconsistencies in decision-making where different reviewers apply different standards.
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
By training random forests on sequential time windows of decision data, we can detect policy drift — changes in the organization's decision-making logic over time. For each time window, we extract the consensus rules and compare them with the rules from the previous window. New rules indicate emerging decision patterns, disappeared rules indicate abandoned practices, and modified rules (same features but shifted thresholds) indicate gradual policy evolution.
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 証拠十分性の予測
決定が承認ゲートに進む前に、ランダム フォレストは証拠バンドルが十分であるかどうかを評価します。証拠の十分性は、現在の証拠が与えられた場合に、設定可能なしきい値と比較して決定が成功する確率として定義されます。
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 証拠と結果のフィードバック ループ
The random forest model is continuously updated with completed decision outcomes, creating a feedback loop between evidence collection and decision success. Over time, the model learns which types of evidence are most predictive of success for each decision type, organizational context, and risk level. This learning is surfaced to decision proposers as evidence preparation guidelines: before submitting a decision proposal, the proposer receives a personalized checklist of recommended evidence items based on the random forest's analysis of what evidence matters most for their specific decision context.
9. Complementary Relationship with Gradient Boosting
Random forests and gradient boosting are not competitors within the Decision Layer — they are complements that serve different functions. This section formalizes their complementary relationship and defines when each algorithm should be preferred.
9.1 Prediction vs Interpretation Tradeoff
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 セットアップ
MARIA OS Enterprise Decision Benchmark (500K レコード、89 個の特徴、時間分割) でランダム フォレストを評価します。ランダム フォレストは、B=500 ツリー、分割ごとに m=sqrt(89)=9 フィーチャ、深さの最大制限なし (純粋な葉または葉ごとに少なくとも 5 つのサンプルが存在するまでツリーは成長します)、およびクラスの重み付けなしで構成されます。 XGBoost (主な代替手段として) と単一のデシジョン ツリー (解釈可能性のベースラインとして) を比較します。
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% |
Random forests are 2-3% less accurate than XGBoost but substantially more accurate than single decision trees. The OOB error estimate (11.4%) is within 0.1% of the true test error (11.3%), confirming the theoretical analysis of OOB unbiasedness.
10.3 Interpretability Evaluation
| 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
この論文では、エージェントの企業インテリジェンス スタックにおける意思決定層の解釈可能性エンジンとしてランダム フォレストを確立しました。勾配ブースティングは優れた予測精度を提供しますが、ランダム フォレストは不可欠な解釈可能機能を提供します。つまり、順列分析による正確な特徴の重要性、ボーンアゲイン ツリーの蒸留によるガバナンス ポリシーの抽出、バッグ外エラー推定による信頼性の高いモデル評価です。
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.
The dual-model architecture within MARIA OS — gradient boosting for prediction, random forests for interpretation — embodies the principle that enterprise AI governance requires both accuracy and transparency. Neither capability alone is sufficient. Accurate predictions without explanations are unauditable. Explanations without accuracy are unreliable. Together, gradient boosting and random forests form a Decision Layer that is both performant and interpretable, enabling MARIA OS to automate decisions at scale while maintaining the governance transparency that enterprise operations demand.
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。