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Predict correlated targets sequentially, using earlier target predictions as input features for subsequent targets to exploit inter-target dependencies
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
Memory-efficient Keras generator that streams sharded CSV files in chunks, renders strokes to images on-the-fly, and yields batches for training on datasets too large for memory
Stack 1D convolutions for local feature extraction before bidirectional LSTMs to classify variable-length stroke sequences into hundreds of doodle categories
Partition a large dataset into N balanced shards using integer key modulo arithmetic for reproducible, class-interleaved splits across CSV files
Normalize raw stroke coordinates to 0-255 range, resample at uniform arc-length spacing, then apply Ramer-Douglas-Peucker simplification
Render stroke sequences to grayscale images with temporal intensity encoding where earlier strokes are brighter and later strokes fade to encode drawing order
Score each face in a video as anomalous by computing L2 distance from the embedding centroid of all faces, then convert to probability via logistic function
| name | tabular-chained-target-prediction |
| description | Predict correlated targets sequentially, using earlier target predictions as input features for subsequent targets to exploit inter-target dependencies |
When multiple targets are correlated (e.g., different engagement metrics for the same entity), predicting them independently wastes inter-target signal. Chained prediction trains target1 first, then feeds its prediction as an additional feature when training target2, and so on. This captures causal or correlational structure between targets without requiring a multi-output model.
import lightgbm as lgb
import numpy as np
targets = ["target1", "target2", "target3", "target4"]
models = {}
feature_cols_base = [c for c in X.columns if c not in targets]
for i, target in enumerate(targets):
feat_cols = feature_cols_base + targets[:i] # add earlier targets as features
model = lgb.LGBMRegressor(**params[target])
model.fit(X_train[feat_cols], y_train[target])
models[target] = model
# Add predictions to both train and test for downstream targets
X_train[target] = np.clip(model.predict(X_train[feat_cols]), 0, 100)
X_test[target] = np.clip(model.predict(X_test[feat_cols]), 0, 100)