### Introduction

Flexible Discriminant Analysis is a classification model based on a mixture of linear regression models, which uses optimal scoring to transform the response variable so that the data are in a better form for linear separation, and multiple adaptive regression splines to generate the discriminant surface.

### Advantages

- Works well with a large number of predictor variables
- Automatically detects interactions between variables
- It is an efficient and fast algorithm, despite its complexity
- Robust to outliers

### Limitations

- Strong sensitivity to configuration setting
- Susceptible to overfitting
- More difficult to understand and interpret than other methods

### Assumptions

No assumptions are made about the distributions of the environmental variables. However, they should not be highly correlated with one another because this could cause problems with the estimation.

### Requires absence data

Yes.

### Configuration options

BCCVL uses the ‘fda’ package, implemented in biomod2. The user can set the following configuration options:

### References

W. Hallgren, F. Santana, S. Low-Choy, Y. Zhao, B. Mackey (2019). Species distribution models can be highly sensitive to algorithm configuration, Ecological Modelling,408. doi.org/10.1016/j.ecolmodel.2019.108719.

Hastie T, Tibshirani R, Friedman J (2009) The elements of statistical learning: data mining, inference and prediction. 2nd edition, Springer.