FieldawareFactorizationMachineClassifier

crank_ml.factorization_machine.fieldaware_factorization_machine_classifier.FieldawareFactorizationMachineClassifier

Fieldaware Factorization Machine for classification.

\[ \hat{y}(x) = w_{0} + \sum_{j=1}^{p} w_{j} x_{j} + \sum_{j=1}^{p} \sum_{j'=j+1}^{p} \langle \mathbf{v}_{j, f_{j'}}, \mathbf{v}_{j', f_j} \rangle x_{j} x_{j'} \]

Where \(\mathbf{v}_{j, f_{j'}}\) is the latent vector corresponding to \(j\) feature for \(f_{j'}\) field, and \(\mathbf{v}_{j', f_j}\) is the latent vector corresponding to \(j'\) feature for \(f_j\) field.

Parameters

Parameter	Description
n_features	Number of input features
embed_dim	(Default: 64) Embedding dimension for the latent variables
n_latent_factors	(Default: 10) The number of latent factors
n_classes	(Default: 2) The number of classes in the classification problem
penalty	(Default: l2) The penalty (aka regularization term) to be used. Defaults to 'l2' which is the standard regularizer for linear SVM models. 'l1' and 'elasticnet' might bring sparsity to the model (feature selection) not achievable with 'l2'. No penalty is added when set to `None``.
alpha	(Default: 0.0001) Constant that multiplies the regularization term. The higher the value, the stronger the regularization.
l1_ratio	(Default: 0.15) The Elastic Net mixing parameter, with 0 <= l1_ratio <= 1. l1_ratio=0 corresponds to L2 penalty, l1_ratio=1 to L1. Only used if penalty is 'elasticnet'.