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# 机器学习代考_Machine Learning代考_ENGG3300 Permutation Feature Importance

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## 机器学习代考_Machine Learning代考_Permutation Feature Importance

The following are the permutation feature importance model’s characteristics.

Post hoc

Global

Model agnostic

Importance based
The permutation feature importance method measures the increase in the model’s error when a particular feature’s value is permuted hence breaking the relationship between the feature and the response variable.
The concept behind permutation feature importance in simple terms can be described as follows.
Input is important to the model if changing the values of that feature increases the model error. A feature is “unimportant” if shuffling its values leaves the model error unchanged because the model ignored the feature for the prediction.
Breiman introduced the permutation feature importance measurement in 2001 for random forests.
The input for the permutation feature importance is a trained model $\mathrm{t}$, a feature matrix $\mathrm{X}$ and target vector $\mathrm{y}$, and an error measure, $\mathrm{E}$.
The original model error can be written as $\mathrm{E}(\mathrm{y}, \mathrm{t}(\mathrm{x}))$.
Estimate the original model error $=\mathrm{E}(\mathrm{y}, \mathrm{t}(\mathrm{X}))$ (e.g., mean squared error)
Then for each feature $s=1 . . . \mathrm{p}$, we run a loop to do the following.

Generate feature matrix $X^{\text {changed }}$ by permuting feature s in the data $\mathrm{X}$. This kills any relationship between s and outcome $\mathrm{y}$.

Estimate error on permuted data $=\mathrm{E}\left(\mathrm{Y}, \mathrm{t}\left(\mathrm{X}^{\text {changed }}\right)\right)$ based on the predictions of the permuted data.

Calculate permutation feature importance.
$\mathrm{FI}^{\mathrm{s}}=$ Error on permuted data / Error on original data.

Easy-to-understand interpretation: the result of permutation importance is easy to understand and present

Feature importance measurements are comparable across different problems

Takes into account all interactions with other features

Permutation feature importance does not require retraining the model

Need access to the true outcome. If someone only provides you with the model and unlabeled data-but not the true outcome, you cannot compute the permutation feature importance.

When the permutation is repeated, the results might vary greatly.

If features are correlated, the permutation feature importance can be biased by unrealistic data instances.

Adding a correlated feature can decrease the importance of the associated feature by splitting the importance between both features.

## 机器学习代考机器学习代考互换特征重要性模型

Breiman在2001年为随机森林引入了互换特征的重要性测量。

$mathrm{FI}^{mathrm{s}}=$ permuted数据的误差/原始数据的误差。

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