Linear Classification
LINEAR CLASSIFICATION | |||
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Parameters | values | Definition | |
Solver | 0 - L2-regularized logistic regression primal,1 - L2-regularized L2-loss SVC dual,2 - L2-regularized L2-loss SVC primal ,3 - L2-regularized L1-loss SVC dual, | We have 8 linear Classification solvers, by combining several types of loss functions and regularization schemes. The regularization can be L1 or L2, and the losses can be the regular L2-loss for SVM (hinge loss), L1-loss for SVM, or the logistic loss for logistic regression. The default value for type is 0. | |
4 - Support Vector Classification by Crammer and Singer | |||
5 - L1-regularized L2-loss SVC6 - L1-regularized Logistic regression ,7 - L2-regularized Logistic regression dual | |||
Cost (C) | Regularization parameter. The strength of the regularization is inversely proportional to C. Must be strictly positive. The penalty is a squared l2 penalty. | ||
Bias | Consider: w_1 * x_1 + w_2 * x_2 + w_3 * x_3 + … + w_bias * x_bias = 0, Here x are the feature values and w are the trained “weights”. The additional feature x_bias is a constant, whose value is equal to the bias. | ||
Termination Criterion | Tolerance for stopping criterion. The stopping tolerance affects the number of iterations used when optimizing the model. | ||
Folds | V-fold for Cross Validation. In v-fold cross-validation, we first divide the training set into v subsets of equal size. Sequentially one subset is tested using the classifier trained on the remaining v − 1 subsets. Thus, each instance of the whole training set is predicted once so the cross-validation accuracy is the percentage of data which are correctly classified. | ||
nr_weight | nr_weight is the number of elements in the array weight_label and weight. Each weight[i] corresponds to weight_label[i], meaning that the penalty of className weight_label[i] is scaled by a factor of weight[i]. | ||
Weight (wi) | Set the parameter C of className i to weight*C, for C-SVC. | ||
Weight_Label | These weights are used to change the penalty for specific labels (classes). If the weight for a label is not changed, it is set to 1.0. | ||