blocks|key|1542816|text|在这里你可以获得一些信息：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1542817|Algorithm+section:+|offset|length|1542818|还有这里|1542819|Sklearn|1542820|entityMap|0|LINK|mutability|MUTABLE|url|http://The+idea+is+the+following:+feature+importance+can+be+measured+by+looking+at+how+much+the+score+(accuracy,+F1,+R%5E2,+etc.+-+any+score+we’re+interested+in)+decreases+when+a+feature+is+not+available.++To+do+that+one+can+remove+feature+from+the+dataset,+re-train+the+estimator+and+check+the+score.+But+it+requires+re-training+an+estimator+for+each+feature,+which+can+be+computationally+intensive.+Also,+it+shows+what+may+be+important+within+a+dataset,+not+what+is+important+within+a+concrete+trained+model.++To+avoid+re-training+the+estimator+we+can+remove+a+feature+only+from+the+test+part+of+the+dataset,+and+compute+score+without+using+this+feature.+It+doesn’t+work+as-is,+because+estimators+expect+feature+to+be+present.+So+instead+of+removing+a+feature+we+can+replace+it+with+random+noise+-+feature+column+is+still+there,+but+it+no+longer+contains+useful+information.+This+method+works+if+noise+is+drawn+from+the+same+distribution+as+original+feature+values+(as+otherwise+estimator+may+fail).+The+simplest+way+to+get+such+noise+is+to+shuffle+values+for+a+feature,+i.e.+use+other+examples’+feature+values+-+this+is+how+permutation+importance+is+computed.++The+method+is+most+suitable+for+computing+feature+importances+when+a+number+of+columns+(features)+is+not+huge;+it+can+be+resource-intensive+otherwise.)[https://eli5.readthedocs.io/en/latest/blackbox/permutation\_importance.html#algorithm](https://eli5.readthedocs.io/en/latest/blackbox/permutation_importance.html#algorithm|1|https://scikit-learn.org/stable/auto_examples/inspection/plot_permutation_importance.html#sphx-glr-auto-examples-inspection-plot-permutation-importance-py^0|0|0|J|0|0|0|0|7|1|0^^$0|@$1|2|3|4|5|6|7|T|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|U|8|@]|9|@$D|V|E|W|1|X]]|A|$]]|$1|F|3|G|5|6|7|Y|8|@]|9|@]|A|$]]|$1|H|3|I|5|6|7|Z|8|@]|9|@$D|10|E|11|1|12]]|A|$]]|$1|J|3|-4|5|6|7|13|8|@]|9|@]|A|$]]]|K|$L|$5|M|N|O|A|$P|Q]]|R|$5|M|N|O|A|$P|S]]]]

Here you can get some info:

<a href="http://The%20idea%20is%20the%20following:%20feature%20importance%20can%20be%20measured%20by%20looking%20at%20how%20much%20the%20score%20(accuracy,%20F1,%20R%5E2,%20etc.%20-%20any%20score%20we%E2%80%99re%20interested%20in)%20decreases%20when%20a%20feature%20is%20not%20available.%20%20To%20do%20that%20one%20can%20remove%20feature%20from%20the%20dataset,%20re-train%20the%20estimator%20and%20check%20the%20score.%20But%20it%20requires%20re-training%20an%20estimator%20for%20each%20feature,%20which%20can%20be%20computationally%20intensive.%20Also,%20it%20shows%20what%20may%20be%20important%20within%20a%20dataset,%20not%20what%20is%20important%20within%20a%20concrete%20trained%20model.%20%20To%20avoid%20re-training%20the%20estimator%20we%20can%20remove%20a%20feature%20only%20from%20the%20test%20part%20of%20the%20dataset,%20and%20compute%20score%20without%20using%20this%20feature.%20It%20doesn%E2%80%99t%20work%20as-is,%20because%20estimators%20expect%20feature%20to%20be%20present.%20So%20instead%20of%20removing%20a%20feature%20we%20can%20replace%20it%20with%20random%20noise%20-%20feature%20column%20is%20still%20there,%20but%20it%20no%20longer%20contains%20useful%20information.%20This%20method%20works%20if%20noise%20is%20drawn%20from%20the%20same%20distribution%20as%20original%20feature%20values%20(as%20otherwise%20estimator%20may%20fail).%20The%20simplest%20way%20to%20get%20such%20noise%20is%20to%20shuffle%20values%20for%20a%20feature,%20i.e.%20use%20other%20examples%E2%80%99%20feature%20values%20-%20this%20is%20how%20permutation%20importance%20is%20computed.%20%20The%20method%20is%20most%20suitable%20for%20computing%20feature%20importances%20when%20a%20number%20of%20columns%20(features)%20is%20not%20huge;%20it%20can%20be%20resource-intensive%20otherwise." rel="nofollow noreferrer">Algorithm section: </a><a href="https://eli5.readthedocs.io/en/latest/blackbox/permutation_importance.html#algorithm" rel="nofollow noreferrer">https://eli5.readthedocs.io/en/latest/blackbox/permutation_importance.html#algorithm</a>

and here

<a href="https://scikit-learn.org/stable/auto_examples/inspection/plot_permutation_importance.html#sphx-glr-auto-examples-inspection-plot-permutation-importance-py" rel="nofollow noreferrer">Sklearn</a>

What is the difference between feature importance and Permutation feature importance? And how can we compute the scores of feature importance in python?

Permutation feature importance

翻译质量差，导致语言生硬或混乱。

没有提供实际的解决方法或示例。

解答不清晰，无法理解或解决问题。

页面排版不美观，阅读体验差。

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特征重要性和排列特征重要性之间的区别是什么？在python中，我们如何计算特征重要性得分？

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