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.

1.07x

Quality

93%

Does it follow best practices?

Impact

100%

1.07x

Average score across 3 eval scenarios

Securityby

Passed

No known issues

Evaluation results

100%

Customer Churn Preprocessing Pipeline

Data splitting and preprocessing pipeline

Criteria

Without context

With context

70/15/15 split

100%

Scaler fit on train only

100%

No all-data fitting

50%

100%

Categorical encoding

100%

Feature scaling applied

100%

Random seed set

100%

Validation set present

100%

Correct pandas loading

100%

Feature/target separation

100%

Fraud Detection Model for Payment Transactions

Class imbalance handling and hyperparameter tuning

Criteria

Without context

With context

Class imbalance addressed

100%

SMOTE or class weights

100%

GridSearch/CV on training only

100%

Test set reserved for final eval

100%

MLflow tracking

100%

MLflow run context

100%

Classification report

100%

AUC-ROC reported

100%

Cross-validation used

100%

Hyperparameters documented

100%

15%

Patient Readmission Risk Prediction with Neural Networks

PyTorch neural network training loop

Criteria

Without context

With context

BatchNorm in hidden layers

100%

Dropout in hidden layers

100%

Adam optimizer used

100%

ReduceLROnPlateau scheduler

100%

Early stopping implemented

100%

Best model checkpoint saved

70%

100%

DataLoader used

100%

Batch size 32

100%

Random seeds set

100%

Separate val loop

100%

Classification report

100%

Validation set included

100%

Repository: secondsky/claude-skills
Commit: 90d6bd7

Evaluated: 29 days ago
Agent: Claude Code
Model: Claude Sonnet 4.6

Table of Contents

Customer Churn Preprocessing Pipeline Fraud Detection Model for Payment Transactions Patient Readmission Risk Prediction with Neural Networks

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