ml-model-training
Train ML models with scikit-learn, PyTorch, TensorFlow. Use for classification/regression, neural networks, hyperparameter tuning, or encountering overfitting, underfitting, convergence issues.
npx skills add majiayu000/claude-skill-registry --skill ml-model-training --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# ML Model Training Train machine learning models with proper data handling and evaluation. ## Training Workflow 1. Data Preparation → 2. Feature Engineering → 3. Model Selection → 4. Training → 5. Evaluation ## Data Preparation ```python import pandas as pd from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler, LabelEncoder # Load and clean data df = pd.read_csv('data.csv') df = df.dropna() # Encode categorical variables le = LabelEncoder() df['category'] = le.fit_transform(df['category']) # Split data (70/15/15) X = df.drop('target', axis=1) y = df['target'] X_train, X_temp, y_train, y_temp = train_test_split(X, y, test_size=0.3) X_val, X_test, y_val, y_test = train_test_split(X_temp, y_temp, test_size=0.5) # Scale features scaler = StandardScaler() X_train = scaler.fit_transform(X_train) X_val = scaler.transform(X_val) X_test = scaler.transform(X_test) ``` ## Scikit-learn Training ```python from sklearn.ensemble import RandomForestClassifier from sklearn.metrics import classification_report, accuracy_score model = RandomForestClassifier(n_estimators=100, random_state=42) model.fit(X_train, y_train) y_pred = model.predict(X_val) pri
- Training Workflow
- Data Preparation
- Scikit-learn Training
- PyTorch Training
- Evaluation Metrics
- Complete Framework Examples
- Best Practices
- Known Issues Prevention
- 1. Data Leakage
- 2. Class Imbalance Ignored
- 3. Overfitting Due to No Regularization
- 4. Not Setting Random Seeds
- 5. Using Test Set for Hyperparameter Tuning
- When to Load References
What does the ml-model-training skill do?
Train ML models with scikit-learn, PyTorch, TensorFlow. Use for classification/regression, neural networks, hyperparameter tuning, or encountering overfitting, underfitting, convergence issues.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill ml-model-training --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.