---
name: ml-pipeline-27bca79a17f6
description: Machine learning pipeline for scientific research including data preprocessing, feature engineering, model selection, training, evaluation, and interpretation. Covers supervised/unsupervised learning, deep learning, cross-validation, hyperparameter tuning, and model explainability. Use when user asks to build a predictive model, classify data, cluster samples, do feature selection, or apply ML to research data. Triggers on "machine learning", "classification", "clustering", "random forest", "neural network", "deep learning", "predict", "feature selection", "cross-validation", "train model".
---

# ML Pipeline

Machine learning for scientific research. Venv: `source /Users/zhangmingda/clawd/.venv/bin/activate`

## Pipeline Overview

```
Data → Clean → Features → Split → Train → Evaluate → Interpret → Report
```

## Model Selection Guide

| Task | Data Size | Interpretability Need | Recommended |
|------|-----------|----------------------|-------------|
| Classification (small) | < 10K | High | Logistic Regression, Decision Tree |
| Classification (medium) | 10K-100K | Medium | Random Forest, XGBoost |
| Classification (large) | > 100K | Low OK | Neural Network, XGBoost |
| Regression (linear) | Any | High | Linear/Ridge/Lasso |
| Regression (nonlinear) | Medium+ | Medium | Random Forest, Gradient Boosting |
| Clustering | Any | Medium | K-Means, DBSCAN, Hierarchical |
| Dimensionality reduction | Any | Medium | PCA, t-SNE, UMAP |
| Anomaly detection | Any | Medium | Isolation Forest, LOF |
| Time series | Any | Varies | ARIMA, Prophet, LSTM |

## Standard Pipeline

```python
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split, cross_val_score, GridSearchCV
from sklearn.preprocessing import StandardScaler, LabelEncoder
from sklearn.metrics import classification_report, confusion_matrix, roc_auc_score
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import Pipeline

# 1. Preprocessing
X = df.drop('target', axis=1)
y = df['target']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)

# 2. Pipeline with scaling
pipe = Pipeline([
    ('scaler', StandardScaler()),
    ('model', RandomForestClassifier(random_state=42))
])

# 3. Cross-validation
scores = cross_val_score(pipe, X_train, y_train, cv=5, scoring='roc_auc')
print(f"CV AUC: {scores.mean():.3f} ± {scores.std():.3f}")

# 4. Hyperparameter tuning
param_grid = {
    'model__n_estimators': [100, 300, 500],
    'model__max_depth': [5, 10, None],
    'model__min_samples_leaf': [1, 5, 10]
}
grid = GridSearchCV(pipe, param_grid, cv=5, scoring='roc_auc', n_jobs=-1)
grid.fit(X_train, y_train)

# 5. Evaluation
y_pred = grid.predict(X_test)
print(classification_report(y_test, y_pred))
print(f"Test AUC: {roc_auc_score(y_test, grid.predict_proba(X_test)[:,1]):.3f}")
```

## Feature Importance & Explainability

```python
# Built-in importance (tree models)
importances = grid.best_estimator_.named_steps['model'].feature_importances_
feat_imp = pd.Series(importances, index=X.columns).sort_values(ascending=False)

# SHAP values (model-agnostic)
# pip install shap
import shap
explainer = shap.TreeExplainer(model)
shap_values = explainer.shap_values(X_test)
shap.summary_plot(shap_values, X_test)
```

## Unsupervised Learning

```python
from sklearn.cluster import KMeans, DBSCAN
from sklearn.decomposition import PCA
from sklearn.manifold import TSNE

# PCA
pca = PCA(n_components=0.95)  # retain 95% variance
X_pca = pca.fit_transform(X_scaled)
print(f"Components: {pca.n_components_}, Explained variance: {pca.explained_variance_ratio_.cumsum()[-1]:.3f}")

# K-Means with elbow method
inertias = [KMeans(n_clusters=k, random_state=42).fit(X_scaled).inertia_ for k in range(2, 11)]

# t-SNE visualization
X_tsne = TSNE(n_components=2, random_state=42, perplexity=30).fit_transform(X_scaled)
```

## Reporting ML Results in Papers

Always include:
1. Dataset description (size, features, class balance)
2. Preprocessing steps
3. Model selection rationale
4. Cross-validation strategy (k-fold, stratified, leave-one-out)
5. Hyperparameter search space and method
6. Multiple metrics (accuracy, precision, recall, F1, AUC)
7. Comparison with baselines
8. Feature importance / model interpretation
9. Confidence intervals or statistical tests on performance
10. Code/data availability statement

## Tips
- Always use stratified splits for imbalanced data
- Report multiple metrics, not just accuracy
- Compare against simple baselines (majority class, mean prediction)
- Use nested CV for unbiased performance estimation
- Check for data leakage (especially with time series)
- Document random seeds for reproducibility


---
Source attribution: beita6969
Original project: https://github.com/beita6969/ScienceClaw/blob/e925f732c7ca435f557e88075bc47a58f1e984f1/skills/ml-pipeline/SKILL.md
License: MIT
License information: https://opensource.org/license/mit
Original upstream instructions; downloaded from Mega AI Hub.

## Upstream license notice

MIT License

Copyright (c) 2025 Peter Steinberger

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.



# Mega AI Hub resource metadata

Creator / publisher: beita6969
Original project: https://github.com/beita6969/ScienceClaw/blob/e925f732c7ca435f557e88075bc47a58f1e984f1/skills/ml-pipeline/SKILL.md
Attribution basis: Source repository record

Fields of work: Data & Analytics, Development & Engineering, Education & Research, Product Management
Roles: Data Analyst, Data Scientist, ML / AI Engineer, Product Manager, Researcher
Specialties: API, Backend
Tools: Shap, Sklearn
Country applicability: Country not specified
Classification basis: Source catalog labels
Instruction SHA-256: 6fc1c0ce0e2b0f6a0da454c5e34012d741f8d08d5267228cd1cd0be5da587e61