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For years, I was hyperparameter tuning XGBoost models wrong. In 3 minutes, I'll share one secret that took me 3 years to figure out. When I did, it cut my training time 10X. Let's dive in. 1. XGBoost: XGBoost (eXtreme Gradient Boosting) is a popular machine learning algorithm, especially for structured (tabular) data. It's claim to fame is winning tons of Kaggle Competitions. But more importantly, it's fast, accurate, and easy to use. But it's also easy to screw it up. 2. Hyperparameter Tuning: To stabilize your XGBoost models, you need to perform hyperparameter tuning. Otherwise XGBoost can overfit your data causing predictions to be horribly wrong on out of sample data. 3. My 3-Year "Beginner" Mistake: XGBoost has tons of parameters. The mistake I was making was treating all of the parameters equally. This caused me hours of tuning my models. And my results weren't half as good until I started doing this. 4. How I improved my hyperparameter tuning: XGBoost has one parameter that rules them all. And after 3 years, I noticed that model stability was 80% driven by this parameter. What was it? Learning rate. When I figured this out that's when things started to change. My models got better. My training times were reduced. Win win. 5. My Simple 2 Step Hyperparameter Tuning Method for XGBoost: What I was doing wrong was doing random grid search over all of the parameters. This took hours. So I made a key change. I began isolating Learning Rate, tuning it first. This was Step 1. The search space for one parameter is super fast to tune. 6. What about the other parameters? Once learning rate was tuned, I then opened the search space to more parameters. This is Step 2. The rest of the parameters have maybe 20% contribution to performance, so that means I can reduce the search space dramatically. 7. The big benefit: Separating tuning into 2 steps cut my training times by a factor of 10X. And my models actually became better. Faster training, better models. Win win. Good luck! === Ready to learn Data Science for Business? I put together a free on-demand workshop that covers the 10 skills that helped me make the transition to Data Scientist: learn.business-science.io/fr… And if you'd like to speed it up, I have a live workshop where I'll share how to use ChatGPT for Data Science: learn.business-science.io/re… If you like this post, please reshare ♻️ it so others can get value.

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