Learn why splitting data for model training and validation is crucial. Understand the pitfalls of using all data for training and validation in achieving accurate model performance.
Table of Contents
Question
You need to train and test your model. You prepared data for model training. You decided to use all the data for model training and then for the model validation. Does this decision help you to achieve your goal?
Answer
No
Explanation
Training and validating a model using the same dataset can lead to overfitting, hindering the model’s ability to generalize to new data. It’s crucial to separate data into training and validation sets. Using all data for training and then validation lacks an independent validation set, risking an inaccurate assessment of the model’s performance on new data.
You have to split your data into two sets: the first is for model training and the second for model testing. If you are using Automated machine learning, it automatically does that for you as part of data preparation and model training.
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