Understanding Machine Learning: The Importance of the 80/20 Split
As we continue to rapidly progress towards an era of artificial intelligence (AI) and data-driven decision making, machine learning has become an increasingly popular tool for businesses across industries to analyze large amounts of data and gain insights. In simple terms, machine learning involves training a computer to learn from data, identify patterns, and make decisions based on that data.
However, a common challenge in machine learning is overfitting – a phenomenon where a model is trained too well on the available data and fails to generalize to new data. This is where the 80/20 split comes into play.
The 80/20 Split
The 80/20 split, also known as the Pareto principle, is a general rule of thumb in data science that suggests splitting your available data into 80% for training and 20% for testing. This split is crucial in avoiding overfitting and ensuring that your model is able to generalize well to new data.
By using a test set that is separate from the training set, you can better evaluate how well your model will perform on new, unseen data. This helps avoid a situation where a model may appear to perform well during training but fails to generalize when used with new data.
Examples of the 80/20 Split in Action
Let’s consider an example in e-commerce. A business may use machine learning to predict which products a customer is most likely to purchase based on their browsing history and previous purchases. By training a model on a dataset that includes 80% of customer data and testing it on the remaining 20%, the business can ensure that the model can accurately predict product recommendations for new customers.
Another example is in fraudulent transaction detection. A financial institution may train a machine learning model to detect potentially fraudulent transactions in their data. By using the 80/20 split to evaluate the model’s performance, the financial institution can ensure that the model can accurately identify fraudulent transactions and reduce the likelihood of financial loss due to fraud.
Conclusion
Underlying the success of machine learning is an understanding of the importance of the 80/20 split. By splitting available data into training and testing sets, businesses can train machine learning models that are better equipped to generalize to new data and make accurate predictions. Employing this best practice can help ensure that your business is making optimal use of machine learning and leveraging the power of data-driven decision making.
