Identify regression machine learning scenarios
Regression models aim to forecast numerical outcomes using training data that encompasses input features along with their corresponding target values. The development of a regression model, as with any supervised learning approach, unfolds through several cycles. In each cycle, you select a suitable algorithm—often configurable with various parameters—to build the model. You then assess how well the model predicts outcomes and adjust it by experimenting with alternative algorithms and tuning the parameters. This iterative process continues until the model reaches a satisfactory level of prediction accuracy.
The overall process for regression training is as follows:
- Divide the training data randomly to form a training set for model development, reserving a portion for model validation. For example, consider setting aside 30-50% of the training data to test against later.
- Employ a fitting algorithm, such as linear...
