Best practices with Qlik AutoML
There are some general guidelines and best practices when working with Qlik AutoML. Following these practices and principles will make it easier to get accurate results and handle the machine learning project flow. The general principles include the following:
- Define the problem: Clearly define the problem you are trying to solve with Qlik AutoML. Identify the variables you want to predict, and understand the available data. This is one of the most important best practices.
- Prepare and clean the data: Ensure that your data is in a format suitable for analysis. This may involve cleaning missing values, handling outliers, transforming variables, cleaning duplicates, and making sure the data is well formatted. This is typically the most time-consuming part of machine learning projects.
- Feature engineering: Explore and create meaningful features from your raw data. Qlik AutoML can automate some feature engineering tasks, but it’s still...
