Optimizes parameters to improve the performance of a learning algorithm
Specifies the hyperplane that represents linear classifiers
Expands the parameter set of a model to improve performance
Takes parameter tuning so far that performance degrades
When a predictive model is accurate but takes too long to run
When a model learns specifics of the training data that do not generalize well to a new data set
When you apply a powerful deep learning algorithm to a simple machine learning problem
When you perform hyperparameter tuning and performance degrades
Predicts numeric responses such as changes in temperature, date, or time
Assigns data to a predefined category
Finds patterns by clustering responses in groups based on similarity
Compares predicted data classifications to the actual class labels in the data
To test whether a model is superfitting the data
To measure the complexity of a model
To evaluate the model fit after training is complete
To test how well a model generalizes during training