Ml Model Training Skill for Claude

Train ML models with scikit-learn, PyTorch, TensorFlow. Use for classification/regression, neural networks, hyperparameter tuning, or encountering overfitting, underfitting, convergence issues.

5k tokens

context cost

the whole folder, loaded on every use

files

instructions only

copies elsewhere

how many repositories repackaged it

202

stars on the repo

on the repository, not the skill itself

Install

one command, takes just this skill from the repository

npx skills add https://github.com/secondsky/claude-skills --skill ml-model-training

The instruction itself

15 sections, as written by the author

ML Model Training

Train machine learning models with proper data handling and evaluation.

Training Workflow

Data Preparation → 2. Feature Engineering → 3. Model Selection → 4. Training → 5. Evaluation

Data Preparation

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

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)
print(classification_report(y_val, y_pred))

PyTorch Training

import torch
import torch.nn as nn

class Model(nn.Module):
    def __init__(self, input_dim):
        super().__init__()
        self.layers = nn.Sequential(
            nn.Linear(input_dim, 64),
            nn.ReLU(),
            nn.Dropout(0.3),
            nn.Linear(64, 32),
            nn.ReLU(),
            nn.Linear(32, 1),
            nn.Sigmoid()
        )

    def forward(self, x):
        return self.layers(x)

model = Model(X_train.shape[1])
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
criterion = nn.BCELoss()

for epoch in range(100):
    model.train()
    optimizer.zero_grad()
    output = model(X_train_tensor)
    loss = criterion(output, y_train_tensor)
    loss.backward()
    optimizer.step()

Evaluation Metrics

| Task | Metrics |

|------|---------|

| Classification | Accuracy, Precision, Recall, F1, AUC-ROC |

| Regression | MSE, RMSE, MAE, R² |

Complete Framework Examples

PyTorch: See references/pytorch-training.md for complete training with:
Custom model classes with BatchNorm and Dropout
Training/validation loops with early stopping
Learning rate scheduling
Model checkpointing
Full evaluation with classification report

TensorFlow/Keras: See references/tensorflow-keras.md for:
Sequential model architecture
Callbacks (EarlyStopping, ReduceLROnPlateau, ModelCheckpoint, TensorBoard)
Training history visualization
TFLite conversion for mobile deployment
Custom training loops

Best Practices

Do:

Use cross-validation for robust evaluation
Track experiments with MLflow
Save model checkpoints regularly
Monitor for overfitting
Document hyperparameters
Use 70/15/15 train/val/test split

Don't:

Train without a validation set
Ignore class imbalance
Skip feature scaling
Use test set for hyperparameter tuning
Forget to set random seeds

Known Issues Prevention

1. Data Leakage

Problem: Scaling or transforming data before splitting leads to test set information leaking into training.

Solution: Always split data first, then fit transformers only on training data:

# ✅ Correct: Fit on train, transform train/val/test
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_val = scaler.transform(X_val)  # Only transform
X_test = scaler.transform(X_test)  # Only transform

# ❌ Wrong: Fitting on all data
X_all = scaler.fit_transform(X)  # Leaks test info!

2. Class Imbalance Ignored

Problem: Training on imbalanced datasets (e.g., 95% class A, 5% class B) leads to models that predict only the majority class.

Solution: Use class weights or resampling:

from sklearn.utils.class_weight import compute_class_weight

# Compute class weights
class_weights = compute_class_weight('balanced', classes=np.unique(y_train), y=y_train)
model = RandomForestClassifier(class_weight='balanced')

# Or use SMOTE for oversampling minority class
from imblearn.over_sampling import SMOTE
smote = SMOTE()
X_resampled, y_resampled = smote.fit_resample(X_train, y_train)

3. Overfitting Due to No Regularization

Problem: Complex models memorize training data, perform poorly on validation/test sets.

Solution: Add regularization techniques:

# Dropout in PyTorch
nn.Dropout(0.3)

# L2 regularization in scikit-learn
RandomForestClassifier(max_depth=10, min_samples_split=20)

# Early stopping in Keras
from tensorflow.keras.callbacks import EarlyStopping
early_stop = EarlyStopping(monitor='val_loss', patience=10, restore_best_weights=True)
model.fit(X_train, y_train, validation_data=(X_val, y_val), callbacks=[early_stop])

4. Not Setting Random Seeds

Problem: Results are not reproducible across runs, making debugging and comparison impossible.

Solution: Set all random seeds:

import random
import numpy as np
import torch

random.seed(42)
np.random.seed(42)
torch.manual_seed(42)
if torch.cuda.is_available():
    torch.cuda.manual_seed_all(42)

5. Using Test Set for Hyperparameter Tuning

Problem: Optimizing hyperparameters on test set leads to overfitting to test data.

Solution: Use validation set for tuning, test set only for final evaluation:

from sklearn.model_selection import GridSearchCV

# ✅ Correct: Tune on train+val, evaluate on test
param_grid = {'n_estimators': [50, 100, 200], 'max_depth': [5, 10, 15]}
grid_search = GridSearchCV(RandomForestClassifier(), param_grid, cv=5)
grid_search.fit(X_train, y_train)  # Cross-validation on training set
best_model = grid_search.best_estimator_

# Final evaluation on held-out test set
final_score = best_model.score(X_test, y_test)

When to Load References

Load reference files when you need:

PyTorch implementation details: Load references/pytorch-training.md for complete training loops with early stopping, learning rate scheduling, and checkpointing
TensorFlow/Keras patterns: Load references/tensorflow-keras.md for callback usage, custom training loops, and mobile deployment with TFLite

Other skills for the same job

different authors, same section of the catalogue

Skill Creator

by anthropics

vendor ×10

Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.

Access NCBI GEO for gene expression/genomics data. Search/download microarray and RNA-seq datasets (GSE, GSM, GPL), retrieve SOFT/Matrix files, for transcriptomics and expression analysis.

12k tokens

Pymc Bayesian Modeling

by christophacham

×4

Bayesian modeling with PyMC. Build hierarchical models, MCMC (NUTS), variational inference, LOO/WAIC comparison, posterior checks, for probabilistic programming and inference.

Multi-objective optimization framework. NSGA-II, NSGA-III, MOEA/D, Pareto fronts, constraint handling, benchmarks (ZDT, DTLZ), for engineering design and optimization problems.

Statistical modeling toolkit. OLS, GLM, logistic, ARIMA, time series, hypothesis tests, diagnostics, AIC/BIC, for rigorous statistical inference and econometric analysis.

Add unsigned integer (uint) type support to PyTorch operators by updating AT_DISPATCH macros. Use when adding support for uint16, uint32, uint64 types to operators, kernels, or when user mentions enabling unsigned types, barebones unsigned types, or uint support.

Convert PyTorch AT_DISPATCH macros to AT_DISPATCH_V2 format in ATen C++ code. Use when porting AT_DISPATCH_ALL_TYPES_AND*, AT_DISPATCH_FLOATING_TYPES*, or other dispatch macros to the new v2 API. For ATen kernel files, CUDA kernels, and native operator implementations.

Write docstrings for PyTorch functions and methods following PyTorch conventions. Use when writing or updating docstrings in PyTorch code.

3k tokens

How to use it

Copy the folder

Take secondsky/ml-model-training from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.