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 # Logistic-Regressor
-A binary logistic regressor based on iterative stochastic gradient descent (SGD) with regularization 
+A binary logistic regressor based on iterative stochastic gradient descent (SGD) with regularization and a comparison on the performance with that of sklearn’s SVM
+
+* Dataset
+  * sklearn’s breast cancer data set
+  * https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_breast_cancer.html
+* The iterative solver supports:
+  * a 2D grid search with one dimension being the learning rate α and the other dimension being the regularization weight λ
+  * mini-batch gradient descent with mini-batch size 8
+  * n-fold cross validation with n = 5
+  * augmented feature vector [x, 1] so that weights and bias are treated in a uniform way as [w0, w], where w0 replaces the bias
+* Compare the run time and performance of the logistic regressor based on gradient descent with that of the SVM based on maximizing margin