A good accuracy score in machine learning depends highly on the problem at hand and the dataset being used.
High accuracy is achievable in some situations, while a seemingly modest score could be outstanding in others.
Many times, good accuracy is defined by the end goal of the machine learning algorithm. Is the algorithm good enough to achieve its initial goal?
If so, chasing higher accuracy may not even benefit you or your clients compared to chasing other things like ethical bias and improving infrastructure.
A Deeper Relationship With Accuracy Scoring
For instance, in the world of quantitative trading, or being a quant, a 51% accuracy rate over some extended period of time would lead to significant profits for you and your clients.
This is because even a slight edge in predicting stock movements can translate into substantial gains over time. With enough capital behind you, you’d be the richest guy on Wall Street!
While chasing a higher accuracy score would obviously be beneficial here, even with a modest 51% accuracy, working on latency and infrastructure of your trading platform may end up being more fruitful and something that should be taken into account before spending money on trying to achieve a higher scoring metric.
While sometimes, as machine learning engineers, we quickly fall in love with the first score we see pop out from our algorithm, On your path to a good accuracy score, you should ensure that your modeling techniques are appropriate, logical, and well-tuned.
Simply testing a few different approaches may not be enough to maximize the potential accuracy of your current business situation.
This is why it’s important to thoroughly explore various techniques and fine-tune your model based on the specifics of your problem.
For example, if you’re using something like a gradient-boosted tree, hyperparameter tuning has proven time and time again to be beneficial to achieving a more accurate model.
Even after doing all of these things, it’s still sometimes hard to know if your model is any good and if you can be happy with your model’s performance.
Something that I do when working with a new machine learning algorithm and dataset is consult academic research and papers for relevant scoring metrics and benchmark scores.
This is highly beneficial and something that I’m constantly doing in my day-to-day work, since you will quickly know if your model’s performance is any good.
This will provide you with a baseline to gauge your model’s performance and help you identify areas for improvement.
Additionally, it is essential to consider other performance metrics, such as precision, recall, F1-score, and area under the curve (AUC), as accuracy alone may not provide a comprehensive understanding of your model’s performance.
There is no one-size-fits-all answer to what constitutes a good accuracy score in machine learning. The appropriate score depends on the problem, dataset, and context.
By thoroughly researching and fine-tuning your modeling techniques and considering other performance metrics, you can work towards achieving the best possible outcome for your specific use case.
Other Articles In Our Accuracy Series:
Accuracy is used EVERYWHERE, which is fine because we wrote these articles below to help you understand it
- Can Machine Learning Models Give An Accuracy Of 100
- High Accuracy Low Precision In Machine Learning
- Data Science Accuracy vs. Precision
- Machine Learning: High Training Accuracy And Low Test Accuracy
- Machine Learning: Validation Accuracy
- How can Data Science Improve The Accuracy Of A Simulation?
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