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diff --git a/docs/3-2.lightgbm.md b/docs/3-2.lightgbm.md
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+# LightGBM 기반 난이도 상수 예측 모델
+
+이 문서는 프로젝트의 또 다른 학습 엔진인 LightGBM 모델의 구조, 하이퍼파라미터 및 특징을 설명합니다.
+
+## 1. 모델 개요
+**LightGBM (Light Gradient Boosting Machine)**은 트리 기반 학습 알고리즘으로, XGBoost와 유사하지만 'Leaf-wise' 트리 성장 방식을 사용하여 더 빠르고 메모리 효율적인 학습이 가능합니다. 특히 대규모 데이터셋에서 높은 성능을 발휘하며, 본 프로젝트에서는 XGBoost와의 비교 및 앙상블 가능성을 열어두기 위해 도입되었습니다.
+
+## 2. 하이퍼파라미터 설정
+`train/train_lightgbm.py`에 정의된 주요 설정값은 다음과 같습니다. XGBoost보다 더 깊고 복잡한 트리를 형성하도록 설정되어 있습니다.
+
+| 파라미터 | 설정값 | 설명 |
+| :--- | :--- | :--- |
+| `learning_rate` | 0.02 | 학습률. XGBoost(0.05)보다 낮게 설정하여 더 정밀하게 수렴 |
+| `num_leaves` | 63 | 하나의 트리가 가질 수 있는 최대 잎(Leaf) 수. 복잡한 패턴 학습에 유리 |
+| `n_estimators` | 3000 | 결정 트리의 개수. 충분한 학습을 위해 크게 설정 |
+| `feature_fraction`| 0.9 | 각 트리 학습 시 사용할 피처 비율 (과적합 방지) |
+| `bagging_fraction`| 0.8 | 데이터 샘플링 비율 |
+| `bagging_freq` | 5 | 배깅 수행 빈도 |
+| `early_stopping` | 100 | 검증 오차가 개선되지 않을 경우 학습을 조기 종료하는 라운드 수 |
+
+## 3. LightGBM 모델의 특징
+
+### A. Leaf-wise 성장 방식
+- 대부분의 boosting 알고리즘이 Level-wise(수평 성장) 방식을 사용하는 것과 달리, LightGBM은 **Leaf-wise(수직 성장)** 방식을 사용합니다. 
+- 이 방식은 손실(Loss)을 가장 많이 줄일 수 있는 잎 노드를 계속 분할하므로, 동일한 분할 횟수에서 Level-wise보다 더 낮은 손실을 달성할 수 있습니다.
+
+### B. 실행 속도 및 효율성
+- XGBoost 대비 학습 속도가 매우 빠르며 메모리 사용량이 적습니다.
+- 많은 양의 데이터를 처리할 때 이점이 큽니다.
+
+## 4. 학습 및 검증 프로세스
+XGBoost와 동일한 파이프라인을 따르며, 결과물은 다음과 같이 구분되어 저장됩니다.
+
+- **모델 파일**: `model_lgbm.pkl`
+- **스케일러**: `scaler_lgbm.pkl`
+- **피처 목록**: `features_lgbm.txt`
+- **검증 결과**: `validate.json`, `validate.png`
+
+## 5. 모델 평가
+- LightGBM은 하이퍼파라미터 변화에 민감하므로 과적합(Overfitting) 여부를 `validate.png`를 통해 상시 모니터링해야 합니다.
+- 현재 설정은 높은 `n_estimators`와 낮은 `learning_rate`를 통해 아주 미세한 채보의 차이까지 학습하는 것을 목표로 하고 있습니다.
diff --git a/docs/4. train script.md b/docs/4. train script.md
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+++ b/docs/4. train script.md	
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+# 4. 학습 스크립트 (Training Script) 가이드
+
+이 문서는 `train/` 폴더 내의 학습 스크립트(`train_xgboost.py`, `train_lightgbm.py`)의 구조와 실행 과정을 설명합니다.
+
+## 1. 실행 구조 및 파라미터
+
+학습 스크립트는 명령행 인자(CLI Arguments)를 통해 제어됩니다.
+
+### 실행 예시
+```bash
+python3 train/train_xgboost.py \
+  --workingDir ./output/xgboost \
+  --dataDir ./datas \
+  --trainSize 1000 \
+  --validSize 200
+```
+
+### 파라미터 상세
+- `--workingDir`: 학습 결과물(`model.pkl`, `validate.json` 등)이 저장될 경로입니다.
+- `--dataDir`: 정답 데이터(`measure.csv`)가 위치한 경로입니다.
+- `--trainSize`: 전체 데이터셋 중 학습에 사용할 샘플 수입니다.
+- `--validSize`: 학습에 참여하지 않고 모델 평가(검증)에만 사용할 샘플 수입니다.
+
+## 2. 데이터 처리 및 학습 파이프라인
+
+스크립트 내부의 `train_model` 함수는 다음 순서로 동작합니다.
+
+### A. 데이터 로드 및 매칭
+1. `workingDir`에서 `features.json`(전처리된 피처)을 읽어옵니다.
+2. `dataDir`에서 `measure.csv`(정답 난이도)를 읽어옵니다.
+3. `(songno, difficulty)`를 키로 사용하여 두 데이터를 매칭하고 하나의 데이터셋으로 결합합니다.
+
+### B. 데이터셋 분리 (Train/Valid Split)
+1. 결합된 데이터셋을 `RANDOM_STATE(42)`를 기반으로 무작위로 섞습니다(Shuffle).
+2. 앞에서부터 `TRAIN_SIZE` 만큼을 학습 데이터로, 그 뒤의 `VALID_SIZE` 만큼을 검증 데이터로 엄격히 분리하여 모델의 일반화 성능을 보장합니다.
+
+### C. 정규화 (Scaling)
+1. `StandardScaler`를 사용하여 피처의 평균을 0, 분산을 1로 조정합니다.
+2. `CONTINUE_TRAINING` 옵션이 켜져 있고 기존 `scaler.pkl`이 있다면 이를 로드하여 일관성을 유지합니다.
+
+### D. 모델 학습 및 지속 학습 (Warm Start)
+1. 모델 객체를 생성하거나, 기존 `model.pkl`이 있다면 이를 로드합니다.
+2. 기존 모델이 있는 경우 이전 학습 상태를 유지한 채 새로운 데이터로 가중치를 미세 조정(Fine-tuning)합니다.
+
+## 3. 검증 및 결과 시각화
+
+학습 완료 직후, 모델이 학습 중에 보지 못한 검증 데이터셋(`X_valid`)을 사용하여 성능을 평가합니다.
+
+1. **지표 계산**: MAE(Mean Absolute Error)와 오차 범위 ±0.1 이내의 정확도를 산출합니다.
+2. **validate.json 저장**: 검증된 각 곡의 실제값, 예측값, 오차를 에러 절댓값 내림차순으로 저장합니다.
+3. **validate.png 생성**: 에러의 분포를 한눈에 볼 수 있도록 산점도 그래프를 생성합니다. (X축: 에러 크기 순, Y축: 에러 절댓값)
+
+## 4. 주요 산출물 (Outputs)
+
+학습이 성공하면 `workingDir` 폴더에 다음 파일들이 생성/업데이트됩니다.
+
+| 파일명 | 내용 |
+| :--- | :--- |
+| `model.pkl` | 학습된 모델 바이너리 |
+| `scaler.pkl` | 피처 정규화를 위한 Scaler 객체 |
+| `features.txt` | 학습에 사용된 피처 이름 목록 및 순서 |
+| `validate.json` | 검증 데이터셋에 대한 상세 예측 결과 (JSON) |
+| `validate.png` | 검증 에러 분포 시각화 그래프 (PNG) |
+
+## 5. 주의사항
+- `features.json`이 먼저 생성되어 있어야 학습이 가능합니다 (`preprocess.ts` 선행 필요).
+- `TRAIN_SIZE + VALID_SIZE`가 전체 가용 데이터 수보다 크면 오류가 발생합니다.
diff --git a/output/lightgbm/compare.json b/output/lightgbm/compare.json
deleted file mode 100644
index 72c8fec..0000000
--- a/output/lightgbm/compare.json
+++ /dev/null
@@ -1,9824 +0,0 @@
-{
-  "summary": {
-    "total_compared": 1402,
-    "average_absolute_error": 0.2209804564907278,
-    "timestamp": "2026-04-25T05:04:52.416Z",
-    "script_used": "predict/predict_lightgbm.py"
-  },
-  "details": [
-    {
-      "songno": "43",
-      "diff": "oni",
-      "actual": 8.1,
-      "predicted": 4.8798,
-      "error": 3.2201999999999993
-    },
-    {
-      "songno": "506",
-      "diff": "ura",
-      "actual": 7.5,
-      "predicted": 4.445,
-      "error": 3.0549999999999997
-    },
-    {
-      "songno": "831",
-      "diff": "ura",
-      "actual": 9.5,
-      "predicted": 6.7235,
-      "error": 2.7765000000000004
-    },
-    {
-      "songno": "719",
-      "diff": "oni",
-      "actual": 9.9,
-      "predicted": 7.3567,
-      "error": 2.5433000000000003
-    },
-    {
-      "songno": "1382",
-      "diff": "ura",
-      "actual": 7.6,
-      "predicted": 5.2197,
-      "error": 2.3803
-    },
-    {
-      "songno": "1214",
-      "diff": "oni",
-      "actual": 6,
-      "predicted": 8.3393,
-      "error": 2.3392999999999997
-    },
-    {
-      "songno": "935",
-      "diff": "oni",
-      "actual": 9.5,
-      "predicted": 7.2974,
-      "error": 2.2026000000000003
-    },
-    {
-      "songno": "1375",
-      "diff": "oni",
-      "actual": 1.1,
-      "predicted": 3.2193,
-      "error": 2.1193
-    },
-    {
-      "songno": "1394",
-      "diff": "oni",
-      "actual": 10.3,
-      "predicted": 8.2169,
-      "error": 2.0831
-    },
-    {
-      "songno": "1185",
-      "diff": "oni",
-      "actual": 10.5,
-      "predicted": 8.5389,
-      "error": 1.9611
-    },
-    {
-      "songno": "503",
-      "diff": "oni",
-      "actual": 11.3,
-      "predicted": 9.3965,
-      "error": 1.903500000000001
-    },
-    {
-      "songno": "967",
-      "diff": "oni",
-      "actual": 8.8,
-      "predicted": 6.9016,
-      "error": 1.8984000000000005
-    },
-    {
-      "songno": "1199",
-      "diff": "oni",
-      "actual": 4.3,
-      "predicted": 2.4051,
-      "error": 1.8948999999999998
-    },
-    {
-      "songno": "1090",
-      "diff": "oni",
-      "actual": 4.2,
-      "predicted": 2.3289,
-      "error": 1.8711000000000002
-    },
-    {
-      "songno": "1138",
-      "diff": "oni",
-      "actual": 4.1,
-      "predicted": 5.9044,
-      "error": 1.8044000000000002
-    },
-    {
-      "songno": "663",
-      "diff": "oni",
-      "actual": 4.1,
-      "predicted": 5.8735,
-      "error": 1.7735000000000003
-    },
-    {
-      "songno": "762",
-      "diff": "oni",
-      "actual": 10.3,
-      "predicted": 8.5287,
-      "error": 1.7713
-    },
-    {
-      "songno": "405",
-      "diff": "oni",
-      "actual": 3.1,
-      "predicted": 4.8485,
-      "error": 1.7484999999999995
-    },
-    {
-      "songno": "48",
-      "diff": "ura",
-      "actual": 9.2,
-      "predicted": 7.4876,
-      "error": 1.7123999999999997
-    },
-    {
-      "songno": "117",
-      "diff": "oni",
-      "actual": 8,
-      "predicted": 6.3078,
-      "error": 1.6921999999999997
-    },
-    {
-      "songno": "101",
-      "diff": "oni",
-      "actual": 1.4,
-      "predicted": 3.0673,
-      "error": 1.6673
-    },
-    {
-      "songno": "1118",
-      "diff": "oni",
-      "actual": 1.2,
-      "predicted": 2.8644,
-      "error": 1.6643999999999999
-    },
-    {
-      "songno": "1349",
-      "diff": "oni",
-      "actual": 6.3,
-      "predicted": 4.6405,
-      "error": 1.6594999999999995
-    },
-    {
-      "songno": "144",
-      "diff": "oni",
-      "actual": 2.5,
-      "predicted": 4.1334,
-      "error": 1.6334
-    },
-    {
-      "songno": "1134",
-      "diff": "oni",
-      "actual": 9.6,
-      "predicted": 7.9673,
-      "error": 1.6326999999999998
-    },
-    {
-      "songno": "465",
-      "diff": "oni",
-      "actual": 6.9,
-      "predicted": 8.5207,
-      "error": 1.6206999999999994
-    },
-    {
-      "songno": "1382",
-      "diff": "oni",
-      "actual": 3.2,
-      "predicted": 1.581,
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-      "songno": "259",
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-      "actual": 6.6,
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-      "error": 1.5495
-    },
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-      "songno": "673",
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-      "actual": 8.9,
-      "predicted": 7.3605,
-      "error": 1.5395000000000003
-    },
-    {
-      "songno": "654",
-      "diff": "oni",
-      "actual": 4.5,
-      "predicted": 6.0395,
-      "error": 1.5395000000000003
-    },
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-      "songno": "1303",
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-      "actual": 4.3,
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-      "error": 1.5175999999999998
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-    {
-      "songno": "316",
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-      "actual": 7.1,
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-      "actual": 6.5,
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-      "error": 1.4966
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-      "predicted": 3.8811,
-      "error": 1.4811
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-      "songno": "205",
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-      "actual": 3,
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-      "error": 1.4192
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-      "songno": "42",
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-      "actual": 1.1,
-      "predicted": 2.4733,
-      "error": 1.3733
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-    {
-      "songno": "1282",
-      "diff": "oni",
-      "actual": 3.8,
-      "predicted": 2.4446,
-      "error": 1.3554
-    },
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-      "songno": "88",
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-      "actual": 8.1,
-      "predicted": 6.7654,
-      "error": 1.3346
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-    {
-      "songno": "739",
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-      "predicted": 8.4253,
-      "error": 1.3253000000000004
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-    {
-      "songno": "1010",
-      "diff": "oni",
-      "actual": 4.8,
-      "predicted": 6.1145,
-      "error": 1.3144999999999998
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-    {
-      "songno": "365",
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-      "actual": 10,
-      "predicted": 8.726,
-      "error": 1.2739999999999991
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-    {
-      "songno": "487",
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-      "actual": 3.5,
-      "predicted": 4.7707,
-      "error": 1.2706999999999997
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-      "songno": "976",
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-      "error": 1.2620999999999993
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-      "songno": "1245",
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-      "error": 1.2416
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-      "songno": "1254",
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-      "actual": 5.9,
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-      "songno": "1412",
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-      "actual": 4.3,
-      "predicted": 3.122,
-      "error": 1.178
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-      "songno": "100",
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-      "actual": 5.3,
-      "predicted": 6.4691,
-      "error": 1.1691000000000003
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-      "songno": "36",
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-      "actual": 1.6,
-      "predicted": 2.7677,
-      "error": 1.1677
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-      "songno": "1190",
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-      "actual": 5.5,
-      "predicted": 6.6509,
-      "error": 1.1509
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-    {
-      "songno": "543",
-      "diff": "oni",
-      "actual": 5.5,
-      "predicted": 4.3542,
-      "error": 1.1458000000000004
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-    {
-      "songno": "507",
-      "diff": "ura",
-      "actual": 8,
-      "predicted": 9.1354,
-      "error": 1.1354000000000006
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-    {
-      "songno": "749",
-      "diff": "oni",
-      "actual": 7.9,
-      "predicted": 9.0321,
-      "error": 1.1320999999999994
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\ No newline at end of file
diff --git a/output/lightgbm/model_lgbm.pkl b/output/lightgbm/model_lgbm.pkl
index c75e8a9..6be9302 100644
Binary files a/output/lightgbm/model_lgbm.pkl and b/output/lightgbm/model_lgbm.pkl differ
diff --git a/output/lightgbm/scaler_lgbm.pkl b/output/lightgbm/scaler_lgbm.pkl
index 04d9232..830ceb6 100644
Binary files a/output/lightgbm/scaler_lgbm.pkl and b/output/lightgbm/scaler_lgbm.pkl differ
diff --git a/output/lightgbm2/compare.json b/output/lightgbm2/compare.json
deleted file mode 100644
index d4ca94c..0000000
--- a/output/lightgbm2/compare.json
+++ /dev/null
@@ -1,9824 +0,0 @@
-{
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-    "timestamp": "2026-04-25T05:38:27.953Z",
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\ No newline at end of file
diff --git a/output/lightgbm2/features.json b/output/lightgbm2/features.json
deleted file mode 100644
index f62e775..0000000
--- a/output/lightgbm2/features.json
+++ /dev/null
@@ -1,17594 +0,0 @@
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\ No newline at end of file
diff --git a/output/lightgbm2/features_lgbm.txt b/output/lightgbm2/features_lgbm.txt
deleted file mode 100644
index c3200c2..0000000
--- a/output/lightgbm2/features_lgbm.txt
+++ /dev/null
@@ -1,8 +0,0 @@
-bpm_avg
-bpm_change
-color_complexity
-density_avg
-density_peak
-note_count
-rhythm_complexity
-scroll_change
diff --git a/output/lightgbm2/model_lgbm.pkl b/output/lightgbm2/model_lgbm.pkl
deleted file mode 100644
index 9c39f4e..0000000
Binary files a/output/lightgbm2/model_lgbm.pkl and /dev/null differ
diff --git a/output/lightgbm2/scaler_lgbm.pkl b/output/lightgbm2/scaler_lgbm.pkl
deleted file mode 100644
index 04d9232..0000000
Binary files a/output/lightgbm2/scaler_lgbm.pkl and /dev/null differ
diff --git a/output/lightgbm2/temp.json b/output/lightgbm2/temp.json
deleted file mode 100644
index f62e775..0000000
--- a/output/lightgbm2/temp.json
+++ /dev/null
@@ -1,17594 +0,0 @@
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-]
\ No newline at end of file
diff --git a/output/lightgbm3/compare.json b/output/lightgbm3/compare.json
deleted file mode 100644
index 0d8b042..0000000
--- a/output/lightgbm3/compare.json
+++ /dev/null
@@ -1,9824 +0,0 @@
-{
-  "summary": {
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-    "average_absolute_error": 0.3542819543509273,
-    "timestamp": "2026-04-25T06:33:02.669Z",
-    "script_used": "predict/predict_lightgbm.py"
-  },
-  "details": [
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-      "error": 2.412000000000001
-    },
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-    {
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-    },
-    {
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-    },
-    {
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\ No newline at end of file
diff --git a/output/lightgbm3/features.json b/output/lightgbm3/features.json
deleted file mode 100644
index f62e775..0000000
--- a/output/lightgbm3/features.json
+++ /dev/null
@@ -1,17594 +0,0 @@
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\ No newline at end of file
diff --git a/output/lightgbm3/features_lgbm.txt b/output/lightgbm3/features_lgbm.txt
deleted file mode 100644
index c3200c2..0000000
--- a/output/lightgbm3/features_lgbm.txt
+++ /dev/null
@@ -1,8 +0,0 @@
-bpm_avg
-bpm_change
-color_complexity
-density_avg
-density_peak
-note_count
-rhythm_complexity
-scroll_change
diff --git a/output/lightgbm3/model_lgbm.pkl b/output/lightgbm3/model_lgbm.pkl
deleted file mode 100644
index a557ca6..0000000
Binary files a/output/lightgbm3/model_lgbm.pkl and /dev/null differ
diff --git a/output/lightgbm3/scaler_lgbm.pkl b/output/lightgbm3/scaler_lgbm.pkl
deleted file mode 100644
index 4c5344f..0000000
Binary files a/output/lightgbm3/scaler_lgbm.pkl and /dev/null differ
diff --git a/output/lightgbm3/temp.json b/output/lightgbm3/temp.json
deleted file mode 100644
index f62e775..0000000
--- a/output/lightgbm3/temp.json
+++ /dev/null
@@ -1,17594 +0,0 @@
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-    "note_count": 635,
-    "density_avg": 6.1092246745897,
-    "density_peak": 12,
-    "bpm_avg": 170,
-    "bpm_change": 0,
-    "scroll_change": 0,
-    "rhythm_complexity": 577,
-    "color_complexity": 0.005163154842975053
-  },
-  {
-    "songno": "120",
-    "difficulty": "oni",
-    "note_count": 765,
-    "density_avg": 6.216357950447204,
-    "density_peak": 20,
-    "bpm_avg": 206.61401307189553,
-    "bpm_change": 15,
-    "scroll_change": 224,
-    "rhythm_complexity": 543,
-    "color_complexity": 0.004980770586693651
-  },
-  {
-    "songno": "646",
-    "difficulty": "oni",
-    "note_count": 456,
-    "density_avg": 3.5800807537012114,
-    "density_peak": 8,
-    "bpm_avg": 175,
-    "bpm_change": 0,
-    "scroll_change": 0,
-    "rhythm_complexity": 408,
-    "color_complexity": 0.0015286650613049817
-  },
-  {
-    "songno": "646",
-    "difficulty": "ura",
-    "note_count": 756,
-    "density_avg": 5.935397039030955,
-    "density_peak": 12,
-    "bpm_avg": 175,
-    "bpm_change": 0,
-    "scroll_change": 0,
-    "rhythm_complexity": 717,
-    "color_complexity": 0.008138518238598654
-  },
-  {
-    "songno": "493",
-    "difficulty": "oni",
-    "note_count": 552,
-    "density_avg": 3.9239193083573487,
-    "density_peak": 10,
-    "bpm_avg": 148,
-    "bpm_change": 0,
-    "scroll_change": 1,
-    "rhythm_complexity": 450,
-    "color_complexity": 0.0017604967403042494
-  },
-  {
-    "songno": "493",
-    "difficulty": "ura",
-    "note_count": 1035,
-    "density_avg": 7.357348703170029,
-    "density_peak": 15,
-    "bpm_avg": 148,
-    "bpm_change": 0,
-    "scroll_change": 0,
-    "rhythm_complexity": 953,
-    "color_complexity": 0.012013295995585432
-  },
-  {
-    "songno": "1107",
-    "difficulty": "oni",
-    "note_count": 850,
-    "density_avg": 6.535869996036465,
-    "density_peak": 13,
-    "bpm_avg": 194,
-    "bpm_change": 0,
-    "scroll_change": 0,
-    "rhythm_complexity": 814,
-    "color_complexity": 0.007554047567047632
-  },
-  {
-    "songno": "1113",
-    "difficulty": "oni",
-    "note_count": 561,
-    "density_avg": 4.46087786259542,
-    "density_peak": 9,
-    "bpm_avg": 125,
-    "bpm_change": 0,
-    "scroll_change": 0,
-    "rhythm_complexity": 553,
-    "color_complexity": 0.0028368573020750966
-  },
-  {
-    "songno": "487",
-    "difficulty": "oni",
-    "note_count": 556,
-    "density_avg": 4.5315763787213275,
-    "density_peak": 12,
-    "bpm_avg": 167,
-    "bpm_change": 0,
-    "scroll_change": 0,
-    "rhythm_complexity": 522,
-    "color_complexity": 0.0027883777378602532
-  },
-  {
-    "songno": "487",
-    "difficulty": "ura",
-    "note_count": 806,
-    "density_avg": 6.569155685700342,
-    "density_peak": 12,
-    "bpm_avg": 167,
-    "bpm_change": 0,
-    "scroll_change": 12,
-    "rhythm_complexity": 784,
-    "color_complexity": 0.007836578406899344
-  },
-  {
-    "songno": "478",
-    "difficulty": "oni",
-    "note_count": 555,
-    "density_avg": 5.020559210526316,
-    "density_peak": 12,
-    "bpm_avg": 165,
-    "bpm_change": 0,
-    "scroll_change": 0,
-    "rhythm_complexity": 532,
-    "color_complexity": 0.0026770483889597546
-  },
-  {
-    "songno": "336",
-    "difficulty": "oni",
-    "note_count": 1304,
-    "density_avg": 11.808920081503283,
-    "density_peak": 21,
-    "bpm_avg": 291.5184049079755,
-    "bpm_change": 1,
-    "scroll_change": 1,
-    "rhythm_complexity": 1255,
-    "color_complexity": 0.03172641728093539
-  },
-  {
-    "songno": "450",
-    "difficulty": "oni",
-    "note_count": 950,
-    "density_avg": 6.918238993710691,
-    "density_peak": 15,
-    "bpm_avg": 220,
-    "bpm_change": 0,
-    "scroll_change": 0,
-    "rhythm_complexity": 910,
-    "color_complexity": 0.01523599996929097
-  },
-  {
-    "songno": "322",
-    "difficulty": "oni",
-    "note_count": 607,
-    "density_avg": 4.979395030714651,
-    "density_peak": 13,
-    "bpm_avg": 149.54920922570017,
-    "bpm_change": 15,
-    "scroll_change": 15,
-    "rhythm_complexity": 560,
-    "color_complexity": 0.005248271768336951
-  }
-]
\ No newline at end of file
diff --git a/output/xgboost_copy/features.json b/output/xgboost_copy/features.json
deleted file mode 100644
index 81cbf0f..0000000
--- a/output/xgboost_copy/features.json
+++ /dev/null
@@ -1,74 +0,0 @@
-[
-  {
-    "songno": "XODUS",
-    "difficulty": "oni",
-    "note_count": 1007,
-    "density_avg": 8.14959935897436,
-    "density_peak": 17,
-    "bpm_avg": 202,
-    "bpm_change": 0,
-    "scroll_change": 14,
-    "rhythm_complexity": 868,
-    "color_complexity": 0.012565274045963932
-  },
-  {
-    "songno": "Destructive Little Sister",
-    "difficulty": "oni",
-    "note_count": 1119,
-    "density_avg": 7.917452830188679,
-    "density_peak": 21,
-    "bpm_avg": 247.5,
-    "bpm_change": 0,
-    "scroll_change": 17,
-    "rhythm_complexity": 1079,
-    "color_complexity": 0.016291673920775917
-  },
-  {
-    "songno": "7 Wonders",
-    "difficulty": "oni",
-    "note_count": 794,
-    "density_avg": 5.571929824561403,
-    "density_peak": 12,
-    "bpm_avg": 168,
-    "bpm_change": 0,
-    "scroll_change": 0,
-    "rhythm_complexity": 726,
-    "color_complexity": 0.006656416024691356
-  },
-  {
-    "songno": "7 Wonders",
-    "difficulty": "ura",
-    "note_count": 1207,
-    "density_avg": 8.47017543859649,
-    "density_peak": 15,
-    "bpm_avg": 168,
-    "bpm_change": 0,
-    "scroll_change": 0,
-    "rhythm_complexity": 965,
-    "color_complexity": 0.01463965596985236
-  },
-  {
-    "songno": "Destruction 3 2 1",
-    "difficulty": "oni",
-    "note_count": 1160,
-    "density_avg": 7.46659375,
-    "density_peak": 15,
-    "bpm_avg": 321.3209999999973,
-    "bpm_change": 0,
-    "scroll_change": 5,
-    "rhythm_complexity": 1101,
-    "color_complexity": 0.012682773678672012
-  },
-  {
-    "songno": "Destruction 3 2 1",
-    "difficulty": "ura",
-    "note_count": 1491,
-    "density_avg": 9.59714765625,
-    "density_peak": 20,
-    "bpm_avg": 321.32099999999707,
-    "bpm_change": 0,
-    "scroll_change": 8,
-    "rhythm_complexity": 1417,
-    "color_complexity": 0.028926671611070473
-  }
-]
\ No newline at end of file
diff --git a/script/train.ts b/script/train_feature.ts
similarity index 94%
rename from script/train.ts
rename to script/train_feature.ts
index 9d01125..2e9ed8d 100644
--- a/script/train.ts
+++ b/script/train_feature.ts
@@ -15,7 +15,7 @@ const { values } = parseArgs({
         dataDir: {
             type: "string"
         },
-        trainScript: {
+        script: {
             type: "string"
         },
         trainSize: {
@@ -28,13 +28,13 @@ const { values } = parseArgs({
     allowPositionals: true,
 });
 
-if (!values.dataDir || !values.workingDir || !values.trainScript) {
+if (!values.dataDir || !values.workingDir || !values.script) {
     console.error("--workingDir --dataDir --trainDir");
     process.exit(1);
 }
 
 generateFeatures();
-const child = spawn("python3", [values.trainScript,
+const child = spawn("python3", [values.script,
     "--workingDir", values.workingDir,
     "--dataDir", values.dataDir,
     "--trainSize", (Number(values.trainSize) || 500).toString(),
diff --git a/script/visualize_abs_errors.py b/script/visualize_abs_errors.py
new file mode 100644
index 0000000..abea4d3
--- /dev/null
+++ b/script/visualize_abs_errors.py
@@ -0,0 +1,50 @@
+import os
+import json
+import matplotlib.pyplot as plt
+import pandas as pd
+import seaborn as sns
+
+output_dir = 'output'
+model_dirs = [d for d in os.listdir(output_dir) if os.path.isdir(os.path.join(output_dir, d)) and os.path.exists(os.path.join(output_dir, d, 'compare.json'))]
+model_dirs.sort()
+
+if not model_dirs:
+    print("데이터를 찾을 수 없습니다.")
+    exit()
+
+fig, axes = plt.subplots(len(model_dirs), 1, figsize=(15, 6 * len(model_dirs)), sharex=False)
+if len(model_dirs) == 1:
+    axes = [axes]
+
+for i, model in enumerate(model_dirs):
+    json_path = os.path.join(output_dir, model, 'compare.json')
+    with open(json_path, 'r', encoding='utf-8') as f:
+        data = json.load(f)
+        df = pd.DataFrame(data.get('details', []))
+    
+    # 에러 절댓값 계산 및 내림차순 정렬
+    df['abs_error'] = df['error'].abs()
+    df = df.sort_values('abs_error', ascending=False).reset_index(drop=True)
+    
+    ax = axes[i]
+    # y축에 abs_error를 사용하여 양수 영역만 표시
+    sns.scatterplot(data=df, x=df.index, y='abs_error', ax=ax, alpha=0.6, s=20, color='darkorange')
+    
+    ax.set_title(f'Model: {model} (Sorted by Absolute Error)', fontsize=15, fontweight='bold')
+    ax.set_ylabel('Absolute Error (|Actual - Predicted|)', fontsize=12)
+    ax.set_xlabel(f'Songs (Ordered by Error Magnitude)', fontsize=12)
+    
+    # 가이드 라인 (오차 0.2, 0.5, 1.0 단위)
+    ax.axhline(0.2, color='green', linestyle='--', linewidth=0.8, alpha=0.5, label='Target (0.2)')
+    ax.axhline(0.5, color='blue', linestyle='--', linewidth=0.8, alpha=0.5)
+    ax.axhline(1.0, color='red', linestyle='--', linewidth=0.8, alpha=0.5)
+
+    # y축을 0부터 시작하도록 설정
+    ax.set_ylim(0, 3.5)
+    ax.set_xticks([]) # x축 라벨 제거
+    ax.grid(True, axis='y', alpha=0.3)
+
+plt.tight_layout()
+output_image = 'abs_error_analysis.png'
+plt.savefig(output_image)
+print(f"절댓값 에러 정렬 그래프가 {output_image}에 저장되었습니다.")
diff --git a/train/train_lightgbm.py b/train/train_lightgbm.py
index c96675b..f708da9 100644
--- a/train/train_lightgbm.py
+++ b/train/train_lightgbm.py
@@ -7,6 +7,9 @@ import random
 import joblib
 import numpy as np
 import lightgbm as lgb
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
 from sklearn.preprocessing import StandardScaler
 from sklearn.metrics import mean_absolute_error
 
@@ -82,7 +85,7 @@ def train_model(working_dir: str, data_dir: str):
             key = (songno, diff)
             if key in feature_map:
                 features = [safe_float(feature_map[key].get(k, 0)) for k in feature_names]
-                dataset.append((features, measure))
+                dataset.append((features, measure, songno, diff))
 
     random.shuffle(dataset)
     if len(dataset) < (TRAIN_SIZE + VALID_SIZE):
@@ -91,10 +94,11 @@ def train_model(working_dir: str, data_dir: str):
     train_dataset = dataset[:TRAIN_SIZE]
     valid_dataset = dataset[TRAIN_SIZE:TRAIN_SIZE + VALID_SIZE]
 
-    X_train = np.array([x for x, _ in train_dataset], dtype=np.float32)
-    y_train = np.array([y for _, y in train_dataset], dtype=np.float32)
-    X_valid = np.array([x for x, _ in valid_dataset], dtype=np.float32)
-    y_valid = np.array([y for _, y in valid_dataset], dtype=np.float32)
+    X_train = np.array([x for x, _, _, _ in train_dataset], dtype=np.float32)
+    y_train = np.array([y for _, y, _, _ in train_dataset], dtype=np.float32)
+    X_valid = np.array([x for x, _, _, _ in valid_dataset], dtype=np.float32)
+    y_valid = np.array([y for _, y, _, _ in valid_dataset], dtype=np.float32)
+    valid_info = [(s, d) for _, _, s, d in valid_dataset]
 
     print(f"Train Size: {len(X_train)} | Valid Size: {len(X_valid)} | Features: {len(feature_names)}")
 
@@ -133,6 +137,66 @@ def train_model(working_dir: str, data_dir: str):
 
     print(f"\nMAE: {mae:.4f} | Accuracy (±{ERROR_TOLERANCE}): {accuracy:.4f}")
 
+    # =====================================================
+    # save validate.json
+    # =====================================================
+    validate_details = []
+    for i in range(len(y_valid)):
+        actual = float(y_valid[i])
+        predicted = float(pred[i])
+        songno, diff = valid_info[i]
+        validate_details.append({
+            "songno": songno,
+            "diff": diff,
+            "actual": actual,
+            "predicted": predicted,
+            "error": actual - predicted
+        })
+
+    validate_details.sort(key=lambda x: abs(x["error"]), reverse=True)
+    validate_result = {
+        "summary": {
+            "total_compared": len(y_valid),
+            "average_absolute_error": float(mae),
+            "accuracy": float(accuracy),
+            "timestamp": "now",
+            "script_used": "train/train_lightgbm.py"
+        },
+        "details": validate_details
+    }
+
+    validate_path = os.path.join(working_dir, "validate.json")
+    with open(validate_path, "w", encoding="utf-8") as f:
+        json.dump(validate_result, f, indent=2, ensure_ascii=False)
+    print(f"Validation result saved: {validate_path}")
+
+    # =====================================================
+    # save validate.png
+    # =====================================================
+    try:
+        plt.switch_backend('Agg')
+        df_plot = pd.DataFrame(validate_details)
+        df_plot['abs_error'] = df_plot['error'].abs()
+        df_plot = df_plot.sort_values('abs_error', ascending=False).reset_index(drop=True)
+
+        plt.figure(figsize=(12, 6))
+        sns.scatterplot(data=df_plot, x=df_plot.index, y='abs_error', alpha=0.6, s=20, color='darkorange')
+        plt.axhline(0.2, color='green', linestyle='--', linewidth=0.8, alpha=0.5, label='Target (0.2)')
+        plt.axhline(0.5, color='blue', linestyle='--', linewidth=0.8, alpha=0.5)
+        plt.axhline(1.0, color='red', linestyle='--', linewidth=0.8, alpha=0.5)
+        plt.ylim(0, max(3.5, df_plot['abs_error'].max() + 0.5))
+        plt.title(f'Validation Absolute Error - {os.path.basename(working_dir)}', fontsize=14)
+        plt.xlabel('Samples (Sorted by Error Magnitude)', fontsize=12)
+        plt.ylabel('Absolute Error', fontsize=12)
+        plt.grid(True, axis='y', alpha=0.3)
+        
+        plot_path = os.path.join(working_dir, "validate.png")
+        plt.savefig(plot_path)
+        plt.close()
+        print(f"Validation plot saved: {plot_path}")
+    except Exception as e:
+        print(f"[WARN] Failed to create validation plot: {e}")
+
     joblib.dump(model, model_path)
     joblib.dump(scaler, scaler_path)
     with open(feature_names_path, "w", encoding="utf-8") as f:
diff --git a/train/train_xgboost.py b/train/train_xgboost.py
index 66d0e8f..66af746 100644
--- a/train/train_xgboost.py
+++ b/train/train_xgboost.py
@@ -6,6 +6,9 @@ import os
 import random
 import joblib
 import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
 
 from xgboost import XGBRegressor
 from sklearn.preprocessing import StandardScaler
@@ -177,7 +180,9 @@ def train_model(
 
             dataset.append((
                 features,
-                measure
+                measure,
+                songno,
+                diff
             ))
 
     # =====================================================
@@ -205,25 +210,29 @@ def train_model(
     ]
 
     X_train = np.array(
-        [x for x, _ in train_dataset],
+        [x for x, _, _, _ in train_dataset],
         dtype=np.float32
     )
 
     y_train = np.array(
-        [y for _, y in train_dataset],
+        [y for _, y, _, _ in train_dataset],
         dtype=np.float32
     )
 
     X_valid = np.array(
-        [x for x, _ in valid_dataset],
+        [x for x, _, _, _ in valid_dataset],
         dtype=np.float32
     )
 
     y_valid = np.array(
-        [y for _, y in valid_dataset],
+        [y for _, y, _, _ in valid_dataset],
         dtype=np.float32
     )
 
+    valid_info = [
+        (s, d) for _, _, s, d in valid_dataset
+    ]
+
     print(f"Train Size: {len(X_train)}")
     print(f"Valid Size: {len(X_valid)}")
     print(f"Feature Count: {len(feature_names)}")
@@ -314,6 +323,73 @@ def train_model(
     for name, score in pairs:
         print(f"{name:25} {score:.6f}")
 
+    # =====================================================
+    # save validate.json
+    # =====================================================
+
+    validate_details = []
+    for i in range(len(y_valid)):
+        actual = float(y_valid[i])
+        predicted = float(pred[i])
+        songno, diff = valid_info[i]
+        
+        validate_details.append({
+            "songno": songno,
+            "diff": diff,
+            "actual": actual,
+            "predicted": predicted,
+            "error": actual - predicted
+        })
+
+    # 에러 절댓값 기준 정렬
+    validate_details.sort(key=lambda x: abs(x["error"]), reverse=True)
+
+    validate_result = {
+        "summary": {
+            "total_compared": len(y_valid),
+            "average_absolute_error": float(mae),
+            "accuracy": float(accuracy),
+            "timestamp": "now",
+            "script_used": "train/train_xgboost.py"
+        },
+        "details": validate_details
+    }
+
+    validate_path = os.path.join(working_dir, "validate.json")
+    with open(validate_path, "w", encoding="utf-8") as f:
+        json.dump(validate_result, f, indent=2, ensure_ascii=False)
+
+    print(f"Validation result saved: {validate_path}")
+
+    # =====================================================
+    # save validate.png
+    # =====================================================
+    try:
+        plt.switch_backend('Agg') # GUI 없는 환경 대응
+        df_plot = pd.DataFrame(validate_details)
+        df_plot['abs_error'] = df_plot['error'].abs()
+        df_plot = df_plot.sort_values('abs_error', ascending=False).reset_index(drop=True)
+
+        plt.figure(figsize=(12, 6))
+        sns.scatterplot(data=df_plot, x=df_plot.index, y='abs_error', alpha=0.6, s=20, color='darkorange')
+        
+        plt.axhline(0.2, color='green', linestyle='--', linewidth=0.8, alpha=0.5, label='Target (0.2)')
+        plt.axhline(0.5, color='blue', linestyle='--', linewidth=0.8, alpha=0.5)
+        plt.axhline(1.0, color='red', linestyle='--', linewidth=0.8, alpha=0.5)
+        
+        plt.ylim(0, max(3.5, df_plot['abs_error'].max() + 0.5))
+        plt.title(f'Validation Absolute Error - {os.path.basename(working_dir)}', fontsize=14)
+        plt.xlabel('Samples (Sorted by Error Magnitude)', fontsize=12)
+        plt.ylabel('Absolute Error', fontsize=12)
+        plt.grid(True, axis='y', alpha=0.3)
+        
+        plot_path = os.path.join(working_dir, "validate.png")
+        plt.savefig(plot_path)
+        plt.close()
+        print(f"Validation plot saved: {plot_path}")
+    except Exception as e:
+        print(f"[WARN] Failed to create validation plot: {e}")
+
     # =====================================================
     # save
     # =====================================================
@@ -366,4 +442,4 @@ if __name__ == "__main__":
     train_model(
         args.workingDir,
         args.dataDir
-    )
\ No newline at end of file
+    )