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random forest classifier using alteryx

classification tool | alteryx help

classification tool | alteryx help

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.

machine learning - alteryx

machine learning - alteryx

The Assisted Modeling tool simplifies the model-building process. With Assisted Modeling, you're guided through the process of building and evaluating several predictive models and selecting the one that best suits your business use case. Assisted Modeling helps you identify a target, set data types, select features, select the most relevant algorithms, and build your models.

The Predict tool makes predictions on new data with your model. After you've trained a model or models, you can score your models using the Predict tool. To generate predictions, input a model built using the Machine Learning tools and representative test data.

regression tool | alteryx help

regression tool | alteryx help

Use the Regression tool as part of a machine-learning pipeline to identify a trend. 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 specific parameters. 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.

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