モデルの解釈可能性

機械学習でのモデルの解釈可能性とは

A machine learning algorithm’s interpretability refers to how easy it is for humans to understand the processes it uses to arrive at its outcomes. Until recently, artificial intelligence (AI) algorithms have been notorious for being “black boxes,” providing no way to understand their inner processes and making it difficult to explain resulting insights to regulatory agencies and stakeholders.

Some models, like logistic regression, are considered to be fairly straightforward and therefore highly interpretable, but as you add features or use more complicated machine learning models such as deep learning, interpretability gets more and more difficult.

Why is Model Interpretability Important?

When using an algorithm’s outcomes to make high-stakes decisions, it’s important to know which features the model did and did not take into account. Additionally, if a model isn’t highly interpretable, the business might not be legally permitted to use its insights to make changes to processes. In heavily regulated industries like banking, insurance, and healthcare, it is important to be able to understand the factors that contribute to likely outcomes in order to comply with regulation and industry best practices.

モデルの解釈可能性 + DataRobot

DataRobot には、人間にとって解釈可能性が高いモデルを生成できる以下のような複数のコンポーネントが用意されています。

  • Model Blueprint gives insight into the preprocessing steps that each model uses to arrive at its outcomes, helping you justify the models you build with DataRobot and explain those models to regulatory agencies if needed.
  • Prediction Explanations show the top variables that impact the model’s outcome for each record, allowing you to explain exactly why your model came to its conclusions.
  • 特徴量のフィッティンググラフでは、予測値と実際の値が比較され、重要度に基づいて並べ替えが行われるため、各特徴量に対するモデルの適合度を評価できます。
  • 特徴量の効果グラフでは、どの特徴量がモデルに最も影響し、各特徴量の値の変化がモデルの結果にどのように影響するかが示されます。

DataRobot works to ensure that models are highly interpretable, minimizing model risk and making it easy for any enterprise to comply with regulations and best practices.

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