If you’re new to knowledge science, you might be in the proper place! We’ve created a pocket book for Kaggle Playground Collection — Season 5, Episode 5 by explaining the codes on the most simple degree for you. Should you assume you might have sufficient data on this topic, you possibly can straight take part within the competitors;
if you’re at a newbie degree, we suggest studying the notes right here;
On this article, you possibly can view the pocket book we will probably be analyzing from this hyperlink, and you’ll copy it and make adjustments and enhancements on it!
To start with, we have to clearly outline our drawback. Our objective on this competitors is to foretell the energy burned throughout coaching.
Since our goal is a prediction of a numerical worth (a steady variable), it is a ‘regression’ drawback.
The analysis metric within the competitors is RMSLE (Root Imply Squared Logarithmic Error) utilized in regression issues, which might merely be described as a metric that measures the ratio of the distinction between the precise worth and the expected worth logarithmically.
- EXAMPLE
Precise worth is 100
