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Jadi, kenapa Lasso begitu populer? Salah satu alasannya adalah kemampuannya dalam menangani masalah *multicollinearity*. Ini adalah situasi di mana variabel independen saling berkorelasi tinggi. Dalam regresi linear biasa, *multicollinearity* bisa menyebabkan masalah dalam interpretasi koefisien dan menghasilkan model yang kurang stabil. Lasso, dengan kemampuannya melakukan seleksi fitur, dapat mengurangi dampak *multicollinearity* dan menghasilkan model yang lebih baik. Selain itu, Lasso juga berguna dalam situasi di mana jumlah fitur lebih besar daripada jumlah sampel (misalnya, dalam analisis data genomik). Dengan menyusutkan koefisien yang tidak relevan, Lasso membantu mencegah *overfitting* dan meningkatkan generalisasi model.
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