What are RapidMiner attributes?

What are RapidMiner attributes?

Special Attributes are Attributes with special roles. These are: id, label, prediction, cluster, weight and batch. Also custom roles can be assigned to Attributes. By default all special Attributes are delivered to the output port irrespective of the conditions in the Select Attribute Operator.

How does RapidMiner calculate accuracy?

Accuracy is calculated by taking the percentage of correct predictions over the total number of examples. Correct prediction means examples where the value of the prediction attribute is equal to the value of the label attribute.

What is RapidMiner auto model?

Auto Model is an extension to RapidMiner Studio that accelerates the process of building and validating models. Best of all, it creates a process that you yourself can modify or put into production — there are no black boxes!

How do you use RapidMiner to predict?

Once you have created your model, RapidMiner Go provides two mechanisms for making predictions:

  1. Apply your model: upload a new data set and see the predictions.
  2. Deploy your model: make your model available to other people and software.

What is generate attribute in RapidMiner?

Description. The Generate Attributes operator constructs new attributes from the attributes of the input ExampleSet and arbitrary constants using mathematical expressions. The attribute names of the input ExampleSet might be used as variables in the mathematical expressions for new attributes.

Is RapidMiner any good?

RapidMiner Studio is a great starting point if you want your organization to try and start using data science or statistics. It is easy to use and self explanatory. It also has some great built in algorithms. The Recommendations feature using crowd sourcing is amazing, and will guide with next steps in the process.

What is RapidMiner machine learning?

RapidMiner provides an end-to-end data science platform that’s built to deliver business impact. It unifies data prep, machine learning and model operations to enhance the productivity of users of any skill level across an enterprise.

What is false positive in confusion matrix?

false positives (FP): We predicted yes, but they don’t actually have the disease. (Also known as a “Type I error.”) false negatives (FN): We predicted no, but they actually do have the disease. (Also known as a “Type II error.”)

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