Model complexity refers to the capacity of a model to capture patterns in data, often balancing between underfitting and overfitting. It is a crucial consideration in machine learning, as more complex models can capture more intricate patterns but also risk being less generalizable to unseen data.

Model Complexity

Model complexity

capacity of a model

capture patterns in data

Underfitting occurs when a machine learning model is too simple to capture the underlying patterns in the data, resulting in poor performance on both the training and test datasets. It is often a result of overly simplistic models or insufficient training, leading to high bias and low variance in predictions.

underfitting

Overfitting occurs when a machine learning model learns the training data too well, capturing noise and outliers as if they were true patterns, which results in poor generalization to new, unseen data. It is a critical issue because it can lead to models that perform well on training data but fail to predict accurately when applied to real-world scenarios.

overfitting

Machine learning is a subset of artificial intelligence that involves the use of algorithms and statistical models to enable computers to improve their performance on a task through experience. It leverages data to train models that can make predictions or decisions without being explicitly programmed for specific tasks.

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