我對精準度可解釋性權衡的三個思考：度量相關，模型族專屬，權衡的確存在

• 這個權衡，似乎是metric dependent的
• 可解釋性的刻畫，是model class specific
• 賭博預測，在小範圍的模型下，的確有這個權衡

思考#1：權衡的確發生在R score與平均絕對誤差上

1. 線性回歸 (Linear Regression)
2. 決策樹 (Decision Tree)
3. 梯度提升法 (Gradient Boosting)

#1 線性回歸
Mean Squared Error: 19592.4703292543
R score: 0.40700134640548247
Mean Absolute Error: 103.67180228987019
#2 決策樹
Mean Squared Error: 10880.635297455
R score: 0.6706795022162286
Mean Absolute Error: 73.76311613574498
#3 梯度提升法
Mean Squared Error: 1388.8979420780786
R score: 0.9579626971080454
Mean Absolute Error: 23.81293483364058

思考#2：廣義加性模型，似乎沒有這個權衡

Our results suggest that inductive bias plays a crucial role in what interpretable models learn and that tree-based GAMs represent the best balance of sparsity, fidelity and accuracy and thus appear to be the most trustworthy GAM models.

1. Sparsity: use fewer features to make predictions 用少一點的特徵來做預測
2. Fidelity: true patterns in the data 要能反應數據的真實模式
3. Accuracy: 就是準確度

思考#3：解釋賭博，的確在小範圍有這個權衡

Section 2 discusses the related work in the application of machine learning to understand and interpret gambling behaviour. Section 5 discusses the interpretability of our empirical results, and concludes the need for further research of understanding and measuring algorithm interpretation.

As reported in [15], we polled the audience at a related presentation at the 2016 New Horizons in Responsible Gambling conference to explore the importance of knowledge extraction and algorithm interpretability.

Respondents were asked whether they would prefer a responsible gambling assessment algorithm that provided a 90% accurate assessment of problem gambling risk that they could not unpack or understand, or a model that provided a 75% accurate assessment that was fully interpretable and accountable. Only 20% chose the more accurate model, with 70% preferring to sacrifice 15 percentage points of accuracy for greater interpretability (10% were uncertain or felt it depended on the circumstances).

Building on the work from the live action sports betting dataset available from the Division on Addiction public domain, in [12] nine supervised learning methods were assessed at identifying disordered Internet sports gamblers.

This paper focuses on knowledge extraction by using random forests and artificial neural networks and TREPAN on a new IGT dataset to not only predict, but also describe, self-excluders through knowledge extraction.

而外產生的對合成數據的思考

• 合成數據是根據真實數據來訓練一個「數據集模型」，藉此生成數據
• 模擬數據則是根據數理模型，根據隨機性機率理論，生成數據