In this video, we practice creating a new feature to address skewness and rescale data for better comparability in algorithms that use distance-based calculations. RapidMiner operators used: Generate Attributes, Normalize.
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Optimize Parameters in RapidMiner
Cross-Validation with Rapidminer
Topic Modeling in RapidMiner (LDA)
Text Processing in RapidMiner
Automated Feature Optimization and Engineering
Comparing Regression Results to Classification Results
Using Classification Algorithms in RapidMiner
RapidMiner Regression using Subprocesses
Dealing with Missing Data
Dealing with Outliers
Creating and Transforming Features
Simplifying your Features
Integrating Data with Joins
Select Features and Reduce your Data in RapidMiner
Convert Data Types in RapidMiner
Using Rapidminer Connections to query a SQL Server database
Building your first RapidMiner process
Building Your MLR Excel Prediction Calculator
MLR Excel Dealing with Skewness & Kurtosis
RapidMiner - Download and Install
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