特徴量変数

機械学習での特徴量変数とは

特徴量とは、分析しようとしているオブジェクトの測定可能なプロパティです。データセットでは、特徴量は列として表示されます。

特徴量の例

上の画像は、不運に見舞われたタイタニック号の処女航海の乗客情報を含むパブリックデータセットの一部です。各特徴量、つまり列は、分析に使用できる測定可能なデータである氏名、年齢、性別、料金などを表します。特徴量は「変数」または「属性」と呼ばれることもあります。データセットに含める特徴量は、何の分析を試みるかに応じて大きく異なる場合があります。

Why are Feature Variables Important?

Features are the basic building blocks of datasets. The quality of the features in your dataset has a major impact on the quality of the insights you will gain when you use that dataset for machine learning. Additionally, different business problems within the same industry do not necessarily require the same features, which is why it is important to have a strong understanding of the business goals of your data science project.

You can improve the quality of your dataset’s features with processes like feature selection and feature engineering, which are notoriously difficult and tedious. If these techniques are done well, the resulting optimal dataset will contain all of the essential features that might have bearing on your specific business problem, leading to the best possible model outcomes and the most beneficial insights.

特徴量変数 + DataRobot

Working with features is one of the most time-consuming aspects of traditional data science. DataRobot automatically detects each feature’s data type (categorical, numerical, a date, percentage, etc.) and performs basic statistical analysis (mean, median, standard deviation, and more) on each feature. Additionally, DataRobot automatically generates a histogram, frequent values chart, and count of occurrence table for each feature, as well as providing users with the ability to manually change variable types, allowing you to quickly understand your data and what insights it could yield.

特徴量の例 2

それだけでなく、DataRobot では特徴量の選択と特徴量エンジニアリングが自動的に実行され、データセットごとにさまざまな組み合わせがテストされて、モデルの結果の精度と含まれるデータの関連性が確保されます。

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