ticregression and linearsvc try to fit the training set as best as possiblewhile with low values of the parameter cthe models put more emphasis on finding coefficient vector (wthat is close to zero there is another interesting aspect of how the parameter acts using low values of will cause the algorithms to try to adjust to the "majorityof data pointswhile using higher value of stresses the importance that each individual data point be classified correctly here is an illustration using linearsvc (figure - )in[ ]mglearn plots plot_linear_svc_regularization(figure - decision boundaries of linear svm on the forge dataset for different values of on the lefthand sidewe have very small corresponding to lot of regularization most of the points in class are at the topand most of the points in class are at the bottom the strongly regularized model chooses relatively horizontal linemisclassifying two points in the center plotc is slightly higherand the model focuses more on the two misclassified samplestilting the decision boundary finallyon the righthand sidethe very high value of in the model tilts the decision boundary lotnow correctly classifying all points in class one of the points in class is still misclassifiedas it is not possible to correctly classify all points in this dataset using straight line the model illustrated on the righthand side tries hard to correctly classify all pointsbut might not capture the overall layout of the classes well in other wordsthis model is likely overfitting similarly to the case of regressionlinear models for classification might seem very restrictive in low-dimensional spacesonly allowing for decision boundaries that are straight lines or planes againin high dimensionslinear models for classification supervised learning