Machine Learning and Association Rule Learning

A set of flashcards based on the key terms related to machine learning and association rule learning concepts.

PCA

Principal Component Analysis, a technique used for dimensionality reduction.

LDA

Linear Discriminant Analysis, used to find a linear combination of features that separates two or more classes.

KPCA

Kernel Principal Component Analysis, an extension of PCA using kernel methods for non-linear dimensionality reduction.

Covariance Matrix

A matrix that captures the pairwise covariances between variables.

Hyperparameter Tuning

The process of optimizing the parameters within a machine learning model that are not learned from data.

K-fold Cross Validation

A technique for assessing how the results of a statistical analysis will generalize to an independent data set.

Imputing

The process of replacing missing data with substituted values.

Scaling

The process of transforming features to be on a similar scale.

Support

The proportion of transactions in the dataset that contain a certain item.

Confidence

A measure of the likelihood that an item A is purchased when item B is purchased.

Lift

A measure of how much more likely item B is purchased when item A is purchased, compared to being purchased independently.

Apriori Algorithm

An algorithm for mining frequent itemsets and relevant association rules.

Frequent Itemsets

Itemsets that appear frequently in a dataset, exceeding a user-specified minimum support threshold.

Association Rule Learning

A rule-based machine learning method for discovering interesting relations between variables in large databases.

Grid Search

A method to systematically work through multiple combinations of parameter tunes, cross-validating as it goes to determine which tune gives the best performance.

Random Search

An alternative to grid search that randomly samples parameter combinations to find the best model.

Model Selection

The process of choosing a statistical model from a set of candidate models based on their performance.

Prediction

The process of forecasting future outcomes based on a model.

Testing Data

Data used to assess the performance of a model after it has been trained.