Use the Classification tool as part of a machine-learning pipeline to identify what category a target belongs to. The tool provides several algorithms you can use to train a model. The tool also allows you to tune a model using many parameters.
Configure the parameters. Each algorithm has different parameters from other algorithms. Each algorithm also has both general and advanced parameters. General parameters are integral to creating an accurate model, even for beginners. Advanced parameters might improve accuracy, but require in-depth understanding of what they do.
Class Weight assigns weights to the different classes in the dataset. Random-forest algorithms tend to overvalue prevailing classes, resulting in imbalances. Class Weight helps balance classes in the dataset by assigning additional weight to minority classes. Balancing classes can improve model performance. By default, all classes have a weight of 1.