Ml Model Training

by secondsky88378361314fMIT227 starsListed Oct 8, 2026Updated Oct 8, 2026Repository updated 10 days ago

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

Instructions onlyData & AnalyticsSoftware Development
AI-generated overview

Guides training machine learning models with scikit-learn, PyTorch and TensorFlow, covering data prep, evaluation and pitfalls.

What it does
Provides a step-by-step workflow for training machine learning models: data preparation, feature engineering, model selection, training and evaluation. It includes code patterns for scikit-learn, PyTorch and TensorFlow/Keras, evaluation metric tables, best practices, and fixes for common problems such as data leakage, class imbalance, overfitting and reproducibility. It also points to two reference files with fuller PyTorch and TensorFlow/Keras training examples.
When to use it
Use it when building classification or regression models, neural networks, or when tuning hyperparameters. It is also meant for diagnosing training problems such as overfitting, underfitting or convergence issues.
Requirements
Instructions only; no scripts are shipped. The examples assume Python with pandas, scikit-learn, PyTorch and TensorFlow/Keras, and optionally imblearn and MLflow.

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 pdfrom sklearn.model_selection import train_test_splitfrom sklearn.preprocessing import StandardScaler, LabelEncoder
# Load and clean datadf = pd.read_csv('data.csv')df = df.dropna()
# Encode categorical variablesle = 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 featuresscaler = 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 RandomForestClassifierfrom 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

python
import torchimport 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

TaskMetrics
ClassificationAccuracy, Precision, Recall, F1, AUC-ROC
RegressionMSE, RMSE, MAE, R²

Complete Framework Examples

  • PyTorch: See references/pytorch-training.md [blocked] 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 [blocked] 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:

python
# ✅ Correct: Fit on train, transform train/val/testscaler = StandardScaler()X_train = scaler.fit_transform(X_train)X_val = scaler.transform(X_val)  # Only transformX_test = scaler.transform(X_test)  # Only transform
# ❌ Wrong: Fitting on all dataX_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:

python
from sklearn.utils.class_weight import compute_class_weight
# Compute class weightsclass_weights = compute_class_weight('balanced', classes=np.unique(y_train), y=y_train)model = RandomForestClassifier(class_weight='balanced')
# Or use SMOTE for oversampling minority classfrom imblearn.over_sampling import SMOTEsmote = 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:

python
# Dropout in PyTorchnn.Dropout(0.3)
# L2 regularization in scikit-learnRandomForestClassifier(max_depth=10, min_samples_split=20)
# Early stopping in Kerasfrom tensorflow.keras.callbacks import EarlyStoppingearly_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:

python
import randomimport numpy as npimport 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:

python
from sklearn.model_selection import GridSearchCV
# ✅ Correct: Tune on train+val, evaluate on testparam_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 setbest_model = grid_search.best_estimator_
# Final evaluation on held-out test setfinal_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

Source and attribution

Source:secondsky/claude-skillsinplugins/ml-model-training/skills/ml-model-trainingat commit8837836

License: MIT

Content belongs to its original authors. SourceWeft indexes it from a public repository.

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