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Memilih subset fitur dari dataset asli tanpa mengubah fitur tersebut. Feature selection merupakan salah satu cara untuk meningkatkan akurasi pada sebuah model machine learning. Ultimately, while feature importance governs the assessment of the most valuable features, feature selection leverages this data to optimize the model’s effectiveness, precision, and interpretability by carefully selecting which features to incorporate into its final form.
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Untuk memahami lebih lanjut, penting untuk membedakan antara feature selection dan feature extraction. In the given example of iris dataset, we have four features and one target variable. This article describes the concepts of feature importance and selection, their role in machine learning, the various methods of computing feature importance, and how to go about feature selection in a most effective way.
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It turns out that we need feature importance for feature selection.
By reducing the number of features used in a machine learning model, feature selection improves computational efficiency, helps mitigate overfitting and improves the interpretability of the model. Feature importance vs feature selection: In machine learning, features refer to the individual variables or attributes of a dataset that are utilized to predict the target variable. Fokus utama adalah memilih fitur yang paling relevan.
Feature importance refers to the degree of influence each feature has on the output or prediction made by a classifier or regression model. In short, the feature importance score is used for performing feature selection. In addition to feature selection techniques, feature importance methods are used to rank the importance of individual features in a model. These methods are flexible and consider interactions between features, which can lead to better model performance but at the cost of higher computational intensity.
