Testing Your Models in the Real World
Lab Steps
The hands-on lab is part of this learning path
Ready for the real environment experience?
Description
How do you know that your models will do a good job making predictions on new, unseen data?
This lab will discuss the fundamentals of splitting your data into training, validation, and test data sets and demonstrate the dangers of overreliance on training data to make predictions.
Learning Objectives
Upon completion of this lab you will be able to:
- Import data using pandas
- Prepare data for modeling
- Split the dataset into training and test data
- Tune models using validation data
- Evaluating models on a test dataset
Intended Audience
This lab is intended for:
- Machine learning engineers
- Anyone interested in evaluating machine learning model performance
Prerequisites
You should possess:
- A basic understanding of Python
Calculated Systems was founded by experts in Hadoop, Google Cloud and AWS. Calculated Systems enables code-free capture, mapping and transformation of data in the cloud based on Apache NiFi, an open source project originally developed within the NSA. Calculated Systems accelerates time to market for new innovations while maintaining data integrity. With cloud automation tools, deep industry expertise, and experience productionalizing workloads development cycles are cut down to a fraction of their normal time. The ability to quickly develop large scale data ingestion and processing decreases the risk companies face in long development cycles. Calculated Systems is one of the industry leaders in Big Data transformation and education of these complex technologies.