How To Get High Performing Models In Competitions. Here are some practical tips I’ve accumulated through my Kaggle journey. So, either build your own model or just start from a baseline public kernel, and try implementing these suggestions !
If you recently got started on Kaggle, or if you are an old regular of the platform, you probably wonder how to easily improve the performance of your model. Here are some practical tips I’ve accumulated through my Kaggle journey. So, either build your own model or just start from a baseline public kernel, and try implementing these suggestions !
Although Kaggle’s policy is to never feature twice an identical competition, there are often remakes of very similar problems. For example, some hosts propose a regular challenge on the same theme yearly (NFL’s Big Data Bowl for example), with only small variations, or in some fields (like medical imaging for example) there are a lot of competitions with different targets but very similar spirit.
Reviewing winners’ solutions (always made public after competition ends thanks to the incredible Kaggle community) can therefore be a great plus, as it gives you ideas to get started, and a winning strategy. If you have time to review a lot of them, you will also soon find out that, even in very different competitions, some popular baseline models seem to always do the job well enough :
You can either look for similar competitions on the Kaggle platform directly, or take a look at this great summary by Sundalai Rajkumar.
Reviewing past competitions can also help you get hints on all the other steps explained in the following. For example, getting tips and tricks on preprocessing for similar problems, how people choose their hyperparameters, what additional tools they have implemented in their models to have them win the game, or if they focused on bagging only similar versions of their best models or rather ensembled a melting pot of all available public kernels.
“How’d you get started with machine learning and data science?”: I trained my first model in 2017 on my friend's lounge room floor.
Getting Started with scikit-learn Pipelines for Machine Learning: Building a pipeline from the ground up. (All code in this post is also included in this GitHub repository.)
Most popular Data Science and Machine Learning courses — August 2020. This list was last updated in August 2020 — and will be updated regularly so as to keep it relevant
You got intrigued by the machine learning world and wanted to get started as soon as possible, read all the articles, watched all the videos, but still isn’t sure about where to start, welcome to the club.
Learning is a new fun in the field of Machine Learning and Data Science. In this article, we’ll be discussing 15 machine learning and data science projects.