In the previous post, we used grid search to find the best hyper parameter for the neural network model with R’s caret package. Here, let’s use Python and scikit-learn package to optimise a neural network model. Just like the caret package, scikit-learn has a pre-built function for hyper parameter search. …
Once you finish training the model and are happy with it, you may need to consider saving the model. Otherwise, you will loose the model once you close the session. The model you create in R session is not persistent, only existing in the memory temporarily. Most of the time, …
Writing code to do machine learning is easy. What makes it difficult is the optimisation. By large, there are two ways to optimise your model. Feature selection & transformation Model parameter optimisation Both are hard-core topics and neither can be covered in this post. Feature selection and transformation often require …