Feature Selection and Predictive Modeling with AutoModel in RapidMiner
Building robust analytics model is one of the core skills of Data Scientist. To build various analytics models, data scientists need to provide the right set of inputs(features) to build the model. For selecting the right features, Data Scientist needs to do multiple iterations to find the right combination of features which would best fit the data to the model. What if I tell you that the whole process of feature selection can be automated?
RapidMiner offers you the option of building analytics models, right from loading the data to selecting the important features as well as selecting the right model. RapidMiner autosuggests which features to be used in the model, based on statistical distribution such as correlation, stability etc.
Most interesting part of the Auto Model in RapidMiner it gives the results in graphical as well as tabular format for different ML models. It represents the relative error as well as the runtime of different models graphically.
Also, you can analyze which factors are important for model prediction. The performance of various models is represented graphically. Guess what, all these graphical representations and analysis of the results could be done without writing a single line of code.
In terms of visualization, models like Gradient Boosting trees, Random Forest etc. could easily be visualized using RapidMiner. Auto Model is one of the most exciting features of RapidMiner, since it supports different user groups such as students, researchers as well as industry professionals. Irrespective of your background, one can easily learn data science with the RapidMiner tool as RapidMiner is a low code/no code platform. If you are a student, you can download the student version free of cost.