Machine learning (ML) plays a central role in predictive analytics by enabling systems to learn from data patterns and make predictions or decisions without explicit programming. Here’s how it works:

1. **Training on Historical Data:** Machine learning models are trained on historical data relevant to the predictive task. This data includes features (input variables) and the corresponding outcomes or labels.

2. **Pattern Recognition:** During training, the machine learning algorithm identifies patterns and relationships within the data. It learns to recognize correlations between the input features and the desired outcomes.

3. **Model Building:** Once trained, the ML model builds a representation of the learned patterns. This could be a mathematical model, such as a decision tree, neural network, or regression model, depending on the nature of the problem.

4. **Predictions:** When presented with new, unseen data, the trained model can make predictions or classifications based on the learned patterns. This is the predictive aspect of machine learning in predictive analytics.

5. **Iterative Improvement:** Predictive models can be refined over time as more data becomes available. Continuous learning allows the model to adapt to changing patterns and improve its predictive accuracy.

In the context of predictive analytics, machine learning is applied to various domains, such as finance for credit scoring, healthcare for disease prediction, and business for sales forecasting. It allows organizations to gain insights, make informed decisions, and anticipate future trends based on data-driven predictions.