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Extracting additional information about a model trained with a predictive tool is possible because of the way the predictive tools are built - containing an R tool with R code that executes the core functionality of the tool, such as training a model with provided input data.
Ever wonder what activities you could do in London with a time constraint? How about how to maximize the value of your fantasy football team on a budget? Join my session at Inspire to learn how prescriptive analytics and the Optimization Tool can help you find the best solution to your most pressing questions. I’ll go into more detail on the day, but to whet your appetite, here’s a little taster of what to expect.
How about using Facebook's Prophet package for time series forecasting in Alteryx Designer? With Prophet, you are not stuck with the results of a completely automatic procedure if the forecast is not satisfactory — an analyst with no training in time series methods can improve or tweak forecasts using a variety of easily-interpretable parameters.
Today, we are very excited to announce the public preview of two Alteryx Designer tools that connect to Azure Machine Learning automated ML (Machine Learning), allowing you to easily leverage the power of automated machine learning from inside Designer.
With the Azure Machine Learning Training tool, you can send data directly from Designer to be trained by Azure Machine Learning automated ML. Use the Azure Machine Learning Scoring tool to use the model you trained for predictions on new data.
With the debut of the Python tool in Alteryx Designer 2018.3, many great macros from PDF table extraction to Parquet file integration are born. As a citizen data scientist, the first instinct is to import some great scikit-learn package into the predictive palette. The question is where to start?