Model Training
Model Training is the process of teaching a machine learning model to identify patterns in data and learn parameters that enable it to make predictions or perform a specific task.
What is Model Training?
During training, a model processes data and compares its predictions with the expected outcomes or learning objectives. An optimization algorithm then adjusts the model's parameters to reduce errors or improve performance. The process is repeated over multiple iterations until the model reaches an appropriate level of performance.
Why is Model Training Important?
Model training determines how well a machine learning system can learn from available data and generalize to new inputs. Effective training helps produce models that are accurate, reliable, and suitable for their intended applications while reducing issues such as underfitting and overfitting.
Common use cases
Model training is commonly used for classification, regression, forecasting, recommendation systems, computer vision, natural language processing, and generative AI applications.