Model selection is the process of choosing the most appropriate machine learning model from a set of candidates based on their performance on a given dataset. It involves balancing complexity and accuracy to avoid overfitting or underfitting, often using techniques like cross-validation to assess generalization capability.

Model Selection

Model selection

machine learning model

Performance on a dataset

balancing complexity

Balancing accuracy

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

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

Cross-validation is a statistical method used to estimate the skill of machine learning models by partitioning data into subsets, training the model on some subsets while validating it on others. This technique helps in assessing how the results of a statistical analysis will generalize to an independent data set, thereby preventing overfitting and improving model reliability.

Your Courses

Relevant Degrees

Your Lessons