How do predictive models learn from historical data?

Predictive models learn from historical data by identifying patterns and relationships within that data, then using those patterns to make predictions about new, unseen data. The process typically involves three main stages: data preparation, model training, and evaluation/refinement. During data preparation, the model processes structured or unstructured historical data, identifies relevant features (input variables), and maps them to target outcomes (labels). For example, a model predicting sales might analyze historical sales data alongside features like pricing, seasonality, or marketing spend. The model’s algorithm then iteratively adjusts its internal parameters to minimize the difference between its predictions and the actual outcomes in the training data.

The training phase relies on algorithms designed to detect statistical relationships. For instance, a linear regression model might learn coefficients that weigh the importance of each feature (e.g., a 0.8 coefficient for marketing spend meaning every dollar spent increases sales by 0.8 units). More complex models, like decision trees or neural networks, identify non-linear patterns. A decision tree could split historical data into groups based on thresholds (e.g., “if price > $50, predict lower sales”), while neural networks use layers of interconnected nodes to transform input data into predictions. The model’s performance is measured using a loss function (e.g., mean squared error for regression), and optimization techniques like gradient descent adjust parameters to reduce this loss over multiple iterations.

After training, models are evaluated using holdout datasets (data not seen during training) to ensure they generalize well. For example, a model trained on 2020–2022 sales data might be tested on 2023 data to verify accuracy. If performance is poor (e.g., overfitting to noise in the training data), developers refine the model by adjusting hyperparameters (like tree depth in decision trees), adding regularization to reduce complexity, or engineering new features (e.g., converting timestamps to “weekday/weekend” flags). Tools like cross-validation—training on multiple subsets of data—help identify robustness issues. This iterative process ensures the model captures meaningful patterns rather than memorizing training examples, enabling reliable predictions on new data.