Machine Learning in Demand Forecasting Systems Workflow

Machine Learning Model Training

Type: Save Data Entry

The Machine Learning Model Training workflow step is a crucial process in developing predictive models for business applications. This step involves training machine learning algorithms on a subset of data to determine the optimal model configuration that can generalize well to unseen data. During this process, relevant features are extracted and selected from the available dataset, which may involve data cleaning, normalization, and transformation techniques. The chosen algorithm is then trained using a portion of the data, and its performance is evaluated through various metrics such as accuracy, precision, recall, and F1 score. The trained model's parameters are adjusted based on the evaluation results to maximize its predictive power. This iterative process helps fine-tune the model until it achieves satisfactory performance levels, providing a robust foundation for subsequent deployment and integration with business systems.