What can affect a models effectiveness?

A model’s effectiveness in Machine Learning can be affected by several factors. The quality and size of the dataset play a major role—noisy, biased, or insufficient data makes learning harder. The choice of features also matters; irrelevant or unprocessed features limit performance. Model complexity influences outcomes too; overly simple models underfit, while overly complex ones may overfit. Finally, training methods, hyperparameters, and evaluation techniques can greatly impact how well a model performs on real-world data.

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