The author highlights key aspects of Applied Machine Learning often overlooked in formal Data Science education. These include thoughtful target selection, dealing with imbalanced data, using real-life testing, meaningful performance metrics, and reconsidering the importance of scores. The insights are aimed at helping junior and mid-level data scientists enhance their career. [50 words]
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Machine Learning in Business: 5 Things a Data Science Course Won’t Teach You
The author shares some important aspects of Applied Machine Learning that can be overlooked in formal Data Science education.
1 — Think twice about the target
It’s crucial to focus on the right target when applying Machine Learning to real-world business problems. The target must represent a behavior, not just a data point. For example, in a clothing retailer scenario, the target could be binary (purchase or not), continuous (purchase amount), or trend-based (buying more than usual).
2 — Deal with imbalance
Real-life data is often imbalanced, and it’s important to know how to handle this. Techniques like undersampling, oversampling, or choosing to do nothing can help mitigate the impact of imbalances.
3 — Testing must be real-life
Retaining unseen (testing) data in its original distribution is essential to evaluate the real-life performance of a machine learning model. Avoid re-balancing testing data before splitting into training and testing data.
4 — Use meaningful performance metrics
Accuracy, ROC curve, and Area Under the ROC Curve may not be suitable for imbalanced class models. Precision and Area under the Precision and Recall Curve are more appropriate for scenarios where the behavior of interest is represented by the minority class.
5 — The importance of scores — or not
Instead of using default cut-off values, it’s better to approach binary classification models as ranking tools. Additionally, presenting ranks or deciles to business stakeholders can provide a clearer understanding of performance across different models.
Choosing the right target, proper measurement framework, and thoughtful communication strategies are essential for building successful machine learning models in a business context.
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