<ul><li>Jumping straight into training models without understanding or preprocessing the dataset properly can lead to poor model performance.</li><li>Using accuracy as the only metric, especially when dealing with imbalanced data, can be misleading. It is important to consider other classification metrics like precision, recall, F1-score, and ROC AUC.</li><li>Copying and pasting models without understanding how they work can hinder model performance improvement. It is essential to grasp the underlying concepts and limitations of each model.</li><li>Focusing only on training accuracy and neglecting the performance on real-world data can lead to overfitting. The model should be able to generalize well.</li><li>Getting stuck in tutorials and not building real projects can limit the learning experience. Confidence comes from implementation and practice.</li></ul>

Top 5 ML Mistakes Beginners Make (And How to Fix Them)

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