Discover why distinct datasets for model training and validation are crucial for accurate AI model performance assessment and successful outcomes in your projects.
You need to train and test your model. You prepared data for model training. You decided to use this data for the model training and then for the model validation.
Does this decision help you to achieve your goal?
No. Using the same dataset for both training and validation can lead to overfitting, where the model performs well on training data but poorly on new data. It’s crucial to split the dataset into distinct parts for training and validation to assess the model’s performance accurately.
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 does it for you automatically as part of data preparation and model training.
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