4 tips to improve your Machine Learning model
Your model performance is the end result of combining 2 basic ingredients:
→ a dataset, and
→ an algorithm
If you wanna improve your model results, you need to improve either one of these 2 things.
And here are 4 tips to do so ↓ https://t.co/tNYJ1ZQVdG
Tip #1. Add more samples to the dataset
The more samples you feed to your algorithm, the higher the chances the algorithm picks up the patterns in the data.
If you work with a tabular dataset, this means you wanna have more rows in your data. https://t.co/oOKd60JWus
Tip #2. Add more features to the dataset.
The more features you add, the higher the chances your representation of the problem/phenomena is complete enough, so your algorithm will work.
You can add features by
→ pulling in external data, or
→ with feature engineering. https://t.co/DcVlD1RKmb
Tip #3. Try another algorithm
There are lots of algorithms you can try. However, for most tabular datasets, boosting tree algorithms (e.g. XGBoost) are usually the best.
If a boosting algorithm does not work with your dataset, focus on improving the data, not the model. https://t.co/AfZHU7elCx
Tip #4. Find the best hyper-parameters for your algorithm and dataset.
Every ML algorithm has a set of parameters that you need to set BEFORE you train the model.
You can find the optimal hyper-parameters using a library like Optuna. https://t.co/AwfSD29qOs
Wanna learn how to apply these 4 tips to a real-world dataset?
In The Real-World ML Tutorial, you will do exactly that.
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