# ML Terbimbing: Regresi (Data Science & ML)

> Regresi linear, Ridge, Lasso, ElasticNet, metrik (MSE, RMSE, R²), overfitting, regularisasi

- 24 pertanyaan wawancara
- Mid-Level
- [Pertanyaan Wawancara: Data Science & ML](https://sharpskill.dev/id/technologies/data-science/pertanyaan-wawancara.md)

## 1. Apa tujuan utama dari regresi linear?

**Jawaban**

Regresi linear bertujuan memodelkan hubungan antara variabel dependen (target) dan satu atau lebih variabel independen (features) dengan menemukan garis lurus yang meminimalkan jumlah kesalahan kuadrat. Teknik ini memungkinkan prediksi nilai kontinu dan menjadi dasar bagi banyak algoritma yang lebih kompleks.

## 2. Dalam regresi linear sederhana, apa yang diwakili oleh koefisien beta (β₁)?

**Jawaban**

Koefisien β₁ mewakili kemiringan garis regresi, menunjukkan seberapa besar variabel target berubah untuk setiap kenaikan satu unit pada variabel independen. β₁ positif berarti hubungan positif, sedangkan β₁ negatif menunjukkan hubungan terbalik antara variabel.

## 3. Metode apa yang digunakan untuk menemukan koefisien optimal dalam regresi linear?

**Jawaban**

Metode kuadrat terkecil biasa (OLS) meminimalkan jumlah kuadrat residu, yaitu selisih antara nilai yang diamati dan diprediksi. Pendekatan ini memberikan solusi analitis bentuk tertutup dan merupakan metode standar untuk mengestimasi parameter regresi linear.

## 21 pertanyaan lagi tersedia

- Apa yang diukur oleh koefisien determinasi R² dalam regresi?
- Apa perbedaan antara MSE (Mean Squared Error) dan RMSE (Root Mean Squared Error)?

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