68. Machine learning: Using regularisation to improve accuracy



Many machine learning techniques include an option to fine-tune regularisation. Regularisation helps to avoid over-fitting of the model to the training set at the cost of accuracy of predication for previously unseen samples in the test set. In the logistic regression method that we have been looking at the regularisation term in the model fit is ā€™cā€™. The lower the c value the greater the regularisation. The previous code has been amended below to loop through a series of c values. For each value of c the model fit is run 100 times with different random train/test splits, and the average results are presented. Continue reading “68. Machine learning: Using regularisation to improve accuracy”