CH_04_01: Batch Machine Learning

 Batch Machine Learning

1. Introduction

Batch machine learning refers to a learning paradigm where models are trained on a complete dataset at once rather than in an incremental or real-time manner. It is suitable for static or periodically updated datasets and is widely used in large-scale data processing environments.

2. Characteristics of Batch Learning

Fixed Dataset: The entire dataset is used at once for training.

No Real-Time Updates: The model does not learn from new data dynamically; it requires retraining.

High Computational Cost: Training large datasets at once demands significant computing resources.

Stable Training Process: As the dataset is fixed, the training process is deterministic and repeatable.

Used in Scheduled Training: Often used in environments where data is collected over time and processed periodically.

3. Advantages of Batch Learning

✔ Better Model Stability: No frequent updates reduce overfitting risks.

✔ Scalability: Suitable for large-scale data processing using frameworks like Hadoop and Spark.

✔ Efficient for Periodic Training: Ideal when new data accumulates over a period and can be used for batch retraining.

4. Disadvantages of Batch Learning

✘ High Latency: Since the model is trained periodically, it cannot respond to new patterns in real time.

✘ Computationally Expensive: Processing the entire dataset requires significant time and resources.

✘ Storage Issues: Maintaining large datasets for retraining can be costly.

5. Batch Learning Process

Step 1: Data Collection

Gather historical data for training.

Ensure data is clean and preprocessed.

Step 2: Data Preprocessing

Handle missing values, duplicates, and inconsistencies.

Normalize and scale features.

Perform feature engineering if needed.

Step 3: Model Selection

Choose appropriate algorithms such as:

Linear Regression (for regression tasks)

Decision Trees, Random Forest, or XGBoost (for classification tasks)

Neural Networks (for complex patterns)

Step 4: Model Training

Use the entire dataset to train the model.

Optimize hyperparameters using techniques like Grid Search or Random Search.

Step 5: Model Evaluation

Split data into Training and Test Sets (e.g., 80-20 or 70-30 split).

Use performance metrics like:

Accuracy, Precision, Recall, F1-score (for classification)

Mean Squared Error (MSE), R² Score (for regression)

Step 6: Model Deployment

Once trained, the model is deployed for inference.

It remains static until the next training cycle.

Step 7: Periodic Model Retraining

New data is collected over time.

The model is retrained periodically (e.g., daily, weekly, or monthly).

6. Batch Learning vs. Online Learning

7. Tools & Frameworks for Batch Learning

Apache Spark MLlib – Distributed batch ML for large-scale datasets

Scikit-learn – Classic batch ML models for small to medium datasets

TensorFlow & PyTorch – Deep learning-based batch training

Hadoop with Mahout – Scalable batch ML on Hadoop clusters

8. Use Cases of Batch Machine Learning

✅ Fraud detection in banking (using past transaction data)

✅ Customer segmentation in marketing (using historical purchase data)

✅ Predictive maintenance (analyzing historical sensor data)

✅ Healthcare diagnostics (training on past medical records)