Maximizing Results with the 80/20 Split in Machine Learning

Maximizing Results with the 80/20 Split in Machine Learning

As the adoption of machine learning algorithms continues to accelerate across various industries, businesses are now considering ways to maximize the effectiveness of their machine learning models.

One such strategy that has proven effective is the 80/20 split, which involves dividing your data into two parts: 80% for training and 20% for testing. This technique is based on the Pareto principle which states that 80% of the effects come from 20% of the causes.

But how does this approach lead to better machine learning models? Let’s dive in:

More Data = Better Models?

One misconception in the realm of machine learning is that more data always leads to better models. While it’s true that having more data can enhance the accuracy of your models, there’s a limit to this notion, surpassing which the returns are diminishing.

This is where the 80/20 split comes in. By using 80% of your data for training your machine learning models, you’re essentially giving them more data to learn from, thereby improving their accuracy. The remaining 20% is used to test the models, ensuring that they’re not overfitting (i.e., memorizing the training data without understanding the underlying concepts).

To put it simply, the larger the training data, the better the base model. However, the testing data helps identify and remove any overfitting. Hence, combining these two in the right proportion can lead to a better model performance.

Proper Model Evaluation

After splitting your dataset into 80/20 training and testing data, it’s essential to evaluate your machine learning model’s accuracy based on the test data. You want to make sure that the accuracy on the testing data is within a reasonable range of the training accuracy, or else it might be overfitting. A decline in testing accuracy indicates that the model did not generalize to new data accurately.

Therefore, it’s recommended to test the model’s accuracy on the training set many times, tuning its parameters to improve its accuracy on both sets without overfitting.

Delivering Precise Models

One significant advantage of using the 80/20 split approach in machine learning is that it allows you to deliver models that are more precise. By testing the model on a smaller dataset, you can identify any issues more efficiently and keep iterating until the model achieves the desired performance.

Relevance in the Business World

The 80/20 split is widely used in the business world to optimize marketing campaigns, target customers, and track sales performance. For instance, a company may use the 80/20 split approach to target their most profitable customers, optimize marketing efforts accurately, and track sales performance effectively.

Furthermore, the 80/20 split approach can be applied to logistics, supply chain planning, and inventory management. By focusing on the most efficient and profitable processes, businesses can reduce their operational costs while maximizing their revenue.

Conclusion

To maximize the effectiveness of machine learning models, businesses need to split their data into 80/20 parts, with 80% used for training the models and 20% for testing and evaluating their accuracy. This approach improves accuracy, helps identify overfitting, and delivers more precise models. The 80/20 split approach is a practical solution that can be applied across various industries, from marketing to logistics, and is rapidly becoming a standard for data-driven businesses.