予測

機械学習における予測とは

"Prediction” refers to the output of an algorithm after it has been trained on a historical dataset and applied to new data when forecasting the likelihood of a particular outcome, such as whether or not a customer will churn in 30 days. The algorithm will generate probable values for an unknown variable for each record in the new data, allowing the model builder to identify what that value will most likely be.

The word “prediction” can be misleading. In some cases, it really does mean that you are predicting a future outcome, such as when you’re using machine learning to determine the next best action in a marketing campaign. Other times, though, the “prediction” has to do with, for example, whether or not a transaction that already occurred was fraudulent. In that case, the transaction already happened, but you’re making an educated guess about whether or not it was legitimate, allowing you to take the appropriate action.

Why are Predictions Important?

機械学習モデルの予測により、質問に対して予想される結果を、履歴データを基に高い精度で推測できるようになります。これには顧客離れの可能性、不正請求の可能性など、あらゆるケースが考えられます。これらの予測は、ビジネス上の目に見える価値を生み出すインサイトを与えてくれます。たとえば、モデルにより、ある顧客に解約の可能性があることが予測された場合、この顧客を対象にして特定のコミュニケーションとアウトリーチを行えば、解約を防止できます。

DataRobot + 予測

The DataRobot automated machine learning platform allows users to easily develop models that make highly accurate predictions. It streamlines the data science process so that users get high-quality predictions in a fraction of the time it took using traditional methods, allowing them to more quickly implement those predictions and see the impact on their bottom line.

In order to start making predictions with DataRobot, you need to deploy the model into a production application. For more details, see the deployment wiki entry or the DataRobot model deployment briefing.
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