CS325 Topic 7, Lecture 1

A specification of a mathematical or probabilistic relationship between different variables in a dataset.

It builds algorithms that learn from data and make predictions or decisions based on new unseen data without human intervention.

It involves models trained on labeled data to predict outcomes based on input variables.

It involves models that work on unlabeled data to find hidden patterns or groupings, such as clustering.

It is a type of machine learning where a model learns through trial and error, guided by rewards or penalties.

Producing a model that performs well on training data but poorly on new, unseen data.

Producing a model that does not perform well on training data and does not generalize well to new data.

The difference between the average prediction of the model and the true value, leading to oversimplification and high prediction error.

The variability of model predictions for a given dataset, where high variance leads to poor performance on new data.

The process of selecting a subset of relevant features from the original dataset to improve model efficiency and accuracy.

The process of deriving new features from the original data to enhance the model's performance.

By minimizing bias and variance to reduce total prediction errors.

A dataset used to train and teach the model, typically 70% of the input dataset.

A dataset used to evaluate the model’s performance, typically 30% of the input dataset.

To build a model that learns from historical data and makes accurate predictions or decisions on new data.

It reduces model complexity, enhances computational efficiency, and reduces generalization errors introduced by noise.