特徵篩選在機器學習中扮演著關鍵角色，它有助於提高模型的準確性和效率，而條件熵是信息理論中一個用於評估特徵相關性的指標，它考慮了特徵之間的條件關係，有助於發現與目標變量密切相關的特徵。本研究旨在探討條件熵作為特徵篩選方法在大量類別型變數資料集的應用。以KKbox音樂資料集為例，利用條件熵在類別變數特徵篩選後的結果，評估篩選後的特徵集對模型性能的影響。我們的實驗結果顯示，我們能夠獲得一個具有較少特徵但仍具有良好性能的模型。這表明條件熵可以作為一種有效的特徵篩選方法，幫助我們發現與用戶聽歌行為密切相關的特徵，從而簡化大量資料集並提升模型的運算效率。

Feature selection plays a crucial role in machine learning as it helps enhance the accuracy and efficiency of models. Conditional entropy is an index from information theory used to evaluate the relevance of features, considering the conditional relationships between them. This helps in identifying features that are closely related to the target variable. This study aims to explore the application of conditional entropy as a feature selection method in datasets with a large number of categorical variables. Taking the KKbox music dataset as an example, we evaluate the impact on model performance by assessing the feature set selected through conditional entropy in categorical variable. Our experimental results show that we were able to obtain a model with fewer features but still maintaining good performance. This demonstrates that conditional entropy can serve as an effective feature selection method, helping us to discover features closely related to user listening behavior, thereby simplifying large datasets and enhancing the computational efficiency of the model.

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