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Cómo pedir trabajo con esta skill instalada
Instalar la skill da un método a tu IA. Tu petición todavía debe aportar los hechos del caso, las restricciones y el entregable esperado.
1Nombra la tarea real
Describe la decisión o el entregable, no solo el tema.
2Aporta la información
Añade materiales, público, límites y hechos conocidos.
3Define qué significa acabado
Fija formato, criterios de calidad y comprobaciones.
Petición inicial adaptable
Sustituye los corchetes por tu caso
Cuándo usarla
Use the scikit-learn skill when:
- Building classification or regression models
- Performing clustering or dimensionality reduction
- Preprocessing and transforming data for machine learning
- Evaluating model performance with cross-validation
- Tuning hyperparameters with grid or random search
- Creating ML pipelines for production workflows
- Comparing different algorithms for a task
- Working with both structured (tabular) and text data
- Need interpretable, classical machine learning approaches
Errores que conviene evitar
ConvergenceWarning
Issue: Model didn't converge Solution: Increase max_iter or scale features
model = LogisticRegression(max_iter=1000)
Poor Performance on Test Set
Issue: Overfitting Solution: Use regularization, cross-validation, or simpler model
# Add regularization
model = Ridge(alpha=1.0)
# Use cross-validation
scores = cross_val_score(model, X, y, cv=5)
Memory Error with Large Datasets
Solution: Use algorithms designed for large data
# Use SGD for large datasets
from sklearn.linear_model import SGDClassifier
model = SGDClassifier()
# Or MiniBatchKMeans for clustering
from sklearn.cluster import MiniBatchKMeans
model = MiniBatchKMeans(n_clusters=8, batch_size=100)