Oblimin is a oblique rotation method that allows for correlation or obliqueness between the rotated components. The main objective of Oblimin rotation is to achieve a simpler structure by minimizing the number of variables with high loadings on a component.

Oblique rotation is the rotation method, in which the rotated components are allowed to be correlated with each other.

Odds ratio represents the ratio of the odds of the event occurring for one group compared to another group. In the context of logistic regression, it measures how the odds of the binary outcome change with respect to a one-unit change in the predictor, while holding all other predictor constant.

The optimization algorithm is used during training to adjust the network’s parameters based on the computed gradients. Gradient descent is a common optimization algorithm used for this purpose.

Orthogonal rotation is the rotation method, in which the rotated components are constrained to be orthogonal to each other, meaning they are independent and uncorrelated.

Random forest utilizes an out-of-bag (OOB) evaluation technique. Since each decision tree is trained on a different subset of the training data, the samples not included in a tree’s training subset can be used for evaluation. This provides an unbiased estimate of the model’s performance without the need for a separate validation set.

See Target variable

Overfitting is the situation in which the model is overtrained to the training sample and not generalized to other datasets. Hence, the model is invalid and unusable.

Oversampling is a technique that balances the data by incrementing the size of the minority (rare events) to the same size as the majority.