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ML之shap:基于FIFA 2018 Statistics(2018年俄罗斯世界杯足球赛)球队比赛之星分类预测数据集利用RF随机森林+计算SHAP值单样本力图/依赖关系贡献图可视化实现可解释性之详细攻略
目录
基于FIFA 2018 Statistics(2018年俄罗斯世界杯足球赛)球队比赛之星分类预测数据集利用RF随机森林+计算SHAP值单样本力图可视化实现可解释性
# T1、基于树模型TreeExplainer创建Explainer并计算SHAP值,且进行单个样本力图可视化(分析单个样本预测的解释)
# T2、基于核模型KernelExplainer创建Explainer并计算SHAP值,且进行单个样本力图可视化(分析单个样本预测的解释)
# (1)、基于树模型TreeExplainer创建Explainer并计算SHAP值
# (2)、全验证数据集样本各特征shap值summary_plot可视化
# (3)、依赖关系贡献图dependence_plot可视化
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ML:机器学习可解释性之SHAP值之理解单样本单特征预测
ML之shap:基于FIFA 2018 Statistics(2018年俄罗斯世界杯足球赛)球队比赛之星分类预测数据集利用RF随机森林+计算SHAP值单样本力图可视化实现可解释性之详细攻略
ML之shap:基于FIFA 2018 Statistics(2018年俄罗斯世界杯足球赛)球队比赛之星分类预测数据集利用RF随机森林+计算SHAP值单样本力图可视化实现可解释性之详细攻略实现
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ML之PDP:基于FIFA 2018 Statistics(2018年俄罗斯世界杯足球赛)球队比赛之星分类预测数据集利用DT决策树&RF随机森林+PDP部分依赖图可视化实现模型可解释性之详细攻略
ML之PDP:基于FIFA 2018 Statistics(2018年俄罗斯世界杯足球赛)球队比赛之星分类预测数据集利用DT决策树&RF随机森林+PDP部分依赖图可视化实现模型可解释性之详细攻略实现
Date | Team | Opponent | Goal Scored | Ball Possession % | Attempts | On-Target | Off-Target | Blocked | Corners | Offsides | Free Kicks | Saves | Pass Accuracy % | Passes | Distance Covered (Kms) | Fouls Committed | Yellow Card | Yellow & Red | Red | Man of the Match | 1st Goal | Round | PSO | Goals in PSO | Own goals | Own goal Time |
14-06-2018 | Russia | Saudi Arabia | 5 | 40 | 13 | 7 | 3 | 3 | 6 | 3 | 11 | 0 | 78 | 306 | 118 | 22 | 0 | 0 | 0 | Yes | 12 | Group Stage | No | 0 | ||
14-06-2018 | Saudi Arabia | Russia | 0 | 60 | 6 | 0 | 3 | 3 | 2 | 1 | 25 | 2 | 86 | 511 | 105 | 10 | 0 | 0 | 0 | No | Group Stage | No | 0 | |||
15-06-2018 | Egypt | Uruguay | 0 | 43 | 8 | 3 | 3 | 2 | 0 | 1 | 7 | 3 | 78 | 395 | 112 | 12 | 2 | 0 | 0 | No | Group Stage | No | 0 | |||
15-06-2018 | Uruguay | Egypt | 1 | 57 | 14 | 4 | 6 | 4 | 5 | 1 | 13 | 3 | 86 | 589 | 111 | 6 | 0 | 0 | 0 | Yes | 89 | Group Stage | No | 0 | ||
15-06-2018 | Morocco | Iran | 0 | 64 | 13 | 3 | 6 | 4 | 5 | 0 | 14 | 2 | 86 | 433 | 101 | 22 | 1 | 0 | 0 | No | Group Stage | No | 0 | 1 | 90 |
- df_X Goal Scored Ball Possession % Attempts ... Yellow & Red Red Goals in PSO
- 0 5 40 13 ... 0 0 0
- 1 0 60 6 ... 0 0 0
- 2 0 43 8 ... 0 0 0
- 3 1 57 14 ... 0 0 0
- 4 0 64 13 ... 0 0 0
-
- [5 rows x 18 columns]
- df_y 0 True
- 1 False
- 2 False
- 3 True
- 4 False
- Name: Man of the Match, dtype: bool
- 输出当前测试样本:5
- Goal Scored 2
- Ball Possession % 38
- Attempts 13
- On-Target 7
- Off-Target 4
- Blocked 2
- Corners 6
- Offsides 1
- Free Kicks 18
- Saves 1
- Pass Accuracy % 69
- Passes 399
- Distance Covered (Kms) 148
- Fouls Committed 25
- Yellow Card 1
- Yellow & Red 0
- Red 0
- Goals in PSO 3
- Name: 118, dtype: int64
- 输出当前测试样本的真实label: False
- 输出当前测试样本的的预测概率: [[0.29 0.71]]
- 输出当前测试样本:7
- Goal Scored 0
- Ball Possession % 53
- Attempts 16
- On-Target 4
- Off-Target 10
- Blocked 2
- Corners 7
- Offsides 1
- Free Kicks 20
- Saves 1
- Pass Accuracy % 77
- Passes 466
- Distance Covered (Kms) 107
- Fouls Committed 23
- Yellow Card 1
- Yellow & Red 0
- Red 0
- Goals in PSO 0
- Name: 35, dtype: int64
- 输出当前测试样本的真实label: False
- 输出当前测试样本的的预测概率: [[0.56 0.44]]
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