A machine learning model’s ability to generalize well to previously unknown inputs determines how well it performs. The proper split between training and test data sets is one of the factors that can determine excellent model performance. With a well-designed split, you can ensure that the predictive ability of your model is properly guaranteed, while at the same time avoiding both over- and under-fitting.
The way you split your data set has an impact on how much information the model can learn from and how well you can test its performance. A poor split may lead to:
- Not enough training data: If the training data is too limited, the model may not learn the important trends, resulting in bad performance.
- Inadequate testing data: If the test set is too limited, your assessment metrics may be inaccurate in reflecting the model’s capacity to generalize.
- Bias-Variance tradeoff issues: Finding the correct balance between training and testing data helps to reduce bias and variance, which are…
