Edges represent probabilistic dependencies between nodes. Directed edges indicate causal or direct influences between variables.

PCA seeks to identify the directions of maximum variability in the data. Eigenvectors are the directions in the data that are not affected by a linear transformation, while eigenvalues indicate how much variance is captured by each eigenvector. In PCA, the principal components are the eigenvectors of the covariance matrix, sorted by their corresponding eigenvalues.

Ensemble is a machine learning method that involves combining the predictions of multiple models to produce a more accurate and robust prediction.

The number of base learners in an ensemble is an important consideration. Increasing the ensemble size can lead to better performance up to a point, after which the returns diminish, and the model may become computationally expensive.

Error term (also known as the residual term) represents the discrepancy between the observed values of the target variable and the predicted values obtained from the linear regression model. It captures the part of the target variable that cannot be explained by the linear relationship with the predictors.

Ethical consideration refer to the principles, guidelines, and moral values that govern the responsible and respectful use of data throughout the entire data mining process. It is to ensure that we comply with any requirements and restrictions regarding data access to protect human subjects and avoid violating privacy.

See Model fit metrics

See Model fit metrics

Evidence refers to the observed values or states of certain variables in the Bayesian network. It is used to update the probabilities of other variables in the network.

The proportion of variance explained by each principal component is crucial for understanding the importance of each component in the data. It helps us determine how many principal components to retain for dimensionality reduction while preserving significant information.

EP refers to the ability of a predictive model, especially association model, to explain or predict the variability observed in the data. It measures how well the model captures the underlying relationships between the input variables (predictors) and the target variable.