Why do we split data into train and test sets?
We train the model on training data and test it on unseen testing data to measure how well it generalizes to new data.
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More FAQs in How to Build Your First Data Science Model in Python
The Iris Flower or Titanic datasets are excellent, simple datasets widely used by beginners.
Overfitting occurs when a model learns the training data too well, including its noise, resulting in poor performance on new data.
Common metrics include Accuracy, Precision, Recall, and F1-Score depending on the distribution of classes.
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