# 教師あり機械学習：回帰 (Data Science & ML)

> 線形回帰、Ridge、Lasso、ElasticNet、評価指標（MSE、RMSE、R²）、過学習、正則化

- 24 面接問題
- Mid-Level
- [面接問題: Data Science & ML](https://sharpskill.dev/ja/technologies/data-science/interview-questions.md)

## 1. 線形回帰の主な目的は何ですか？

**回答**

線形回帰は、二乗誤差の合計を最小化する直線を見つけることで、従属変数（ターゲット）と1つ以上の独立変数（特徴量）の関係をモデル化することを目的としています。この手法により連続値の予測が可能となり、多くの複雑なアルゴリズムの基礎を形成します。

## 2. 単純線形回帰において、ベータ係数（β₁）は何を表しますか？

**回答**

β₁係数は回帰直線の傾きを表し、独立変数が1単位増加したときにターゲット変数がどれだけ変化するかを示します。β₁が正の場合は正の関係を意味し、負の場合は変数間の逆の関係を示します。

## 3. 線形回帰で最適な係数を見つけるために使用される手法はどれですか？

**回答**

通常最小二乗法（OLS）は、観測値と予測値の差である残差の二乗和を最小化します。このアプローチは閉形式の解析解を提供し、線形回帰のパラメータを推定する標準的な手法です。

## さらに21問利用可能

- 回帰における決定係数R²は何を測定しますか？
- MSE（Mean Squared Error）とRMSE（Root Mean Squared Error）の違いは何ですか？

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