Recently I wrote a post for DataScience+ (which by the way is a great website for learning about R) explaining how to fit a neural network in R using the neuralnet package, however I glossed over the “how to choose the number of neurons in the hidden layer” part. The glossing over is mainly due to the fact that there is no fixed rule or suggested “best” rule for this task but the mainstream approach (as far as I know) is mostly a trial and error process starting from a set of rules of thumb and a heavy cross validating attitude.

## Monday, 28 September 2015

### Selecting the number of neurons in the hidden layer of a neural network

## Tuesday, 15 September 2015

### Predicting creditability using logistic regression in R: cross validating the classifier (part 2)

Now that we fitted the classifier and run some preliminary tests, in order to get a grasp at how our model is doing when predicting creditability we need to run some cross validation methods.

Cross validation is a model evaluation method that does not use conventional fitting measures (such as R^2 of linear regression) when trying to evaluate the model. Cross validation is focused on the predictive ability of the model. The process it follows is the following: the dataset is splitted in a training and testing set, the model is trained on the testing set and tested on the test set.

Note that running this process one time gives you a somewhat unreliable estimate of the performance of your model since this estimate usually has a non-neglectable variance.

## Wednesday, 2 September 2015

### Predicting creditability using logistic regression in R (part 1)

As I said in the previous post, this summer I’ve been learning some of the most popular machine learning algorithms and trying to apply what I’ve learned to real world scenarios. The German Credit dataset provided by the UCI Machine Learning Repository is another great example of application.

The German Credit dataset contains 1000 samples of applicants asking for some kind of loan and the creditability (either good or bad) alongside with 20 features that are believed to be relevant in predicting creditability. Some examples are: the duration of the loan, the amount, the age of the applicant, the sex, and so on. Note that the dataset contains both categorical and quantitative features.

This is a classical example of a binary classification task, where our goal is essentially to build a model that can improve the selection process of the applicants.