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1 Markets I: The Ecosystem and Exchange-Traded Marketsالأسواق عبر الإنترنت 2 Markets II: Rates, FX and Creditالأسواق عبر الإنترنت 3 Markets III: Commodities, Energy and Cryptoالأسواق عبر الإنترنت 4 Quantitative Methodsالأساليب عبر الإنترنت 5 Derivatives and Volatilityالمشتقات عبر الإنترنت 6 Rates, Credit, XVA and Riskالفائدة والائتمان والمخاطر عبر الإنترنت 7 Research Craft: Predictors, Backtests, Measurement, Portfoliosالبحث عبر الإنترنت 8 Strategies I: Equities and Futuresالاستراتيجيات عبر الإنترنت 9 Strategies II: Volatility, Relative Value, Macro and the Bank Desksالاستراتيجيات عبر الإنترنت 10 Microstructure and Executionالتنفيذ عبر الإنترنت 11 Market Making and High-Frequency Tradingصناعة السوق عبر الإنترنت 12 Machine Learning for Marketsتعلم الآلة عبر الإنترنت 13 Low-Latency Softwareالتكنولوجيا عبر الإنترنت 14 Networks, Hardware and Trading Infrastructureالتكنولوجيا عبر الإنترنت 15 Research, Data and Risk Platformsالتكنولوجيا عبر الإنترنت 16 The Desk and the Firmالشركة عبر الإنترنت 17 The Industry: Firms, Roles and Careersالمسارات المهنية عبر الإنترنت 18 The Interview Bookالمسارات المهنية عبر الإنترنت
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Quantitative Finance · المسرد

ما معنى Shapley value, SHAP value؟

يُعرف أيضًا باسم: Shapley value · SHAP value

Definition 6.5 Machine Learning for Markets · الفصل 6 — Feature Engineering, Selection and Importance

For a game vv on the set PP of players, the Shapley value of player jj is

ϕj=∑S⊆P∖{j}∣S∣! (∣P∣−∣S∣−1)!∣P∣!(v(S∪{j})−v(S)),\phi_j = \sum_{S\subseteq P\setminus\{j\}}\frac{|S|!\,(|P| - |S| - 1)!}{|P|!}\bigl(v(S\cup\{j\}) - v(S)\bigr),

the only allocation that is efficient (∑jϕj=v(P)−v(∅)\sum_j\phi_j = v(P) - v(\emptyset)), symmetric, zero for a player that adds nothing, and additive across games. A SHAP value is the Shapley value of feature jj for one prediction, with v(S)v(S) the model’s expected output when the features in SS are fixed at their values (Lundberg and Lee, 2017); for tree ensembles it is computed exactly and fast (TreeSHAP).

Four importances of the same boosted trees on the planted task, each as a share of its positive total: the four signal features, the copy x_1b, and the largest of the ten noise features. Permutation, drop-column and SHAP are computed on the 20 000 held-out rows, gain on the training rows. Data: ml_importance.importances.
Figure 6.1. Four importances of the same boosted trees on the planted task, each as a share of its positive total: the four signal features, the copy x1bx_{1b}, and the largest of the ten noise features. Permutation, drop-column and SHAP are computed on the 20 000 held-out rows, gain on the training rows. Data: ml_importance.importances.
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