Monte Carlo method is a handy tool for transforming problems of probabilistic nature into deterministic computations using the law of large numbers. Imagine that you want to asses the future value of your investments and see what is the worst-case scenario for a given level of probability. Or that you want to plan the production of your factory given past daily performance of individual workers to ensure that you will meet a tough delivery plan with high enough probability. For such and many more real-life tasks you can use the Monte Carlo method.
Clustering is a hugely important step of exploratory data analysis and finds plenty of great applications. Typically, clustering technique will identify different groups of observations among your data. For example, if you need to perform market segmentation, cluster analysis will help you with labeling each segment so that you can evaluate each segment’s potential and target some attractive segments. Therefore, your marketing program and positioning strategy rely heavily on the very fundamental step – grouping of your observations and creation of meaningful segments. We may also find many more use cases in computer science, biology, medicine or social science. However, it often turns out to be quite difficult to define properly how a well-separated cluster looks like.
Today, I will discuss some technical aspects of hierarchical cluster analysis, namely Agglomerative Clustering. One great advantage of this hierarchical approach would be fully automatic selection of the appropriate number of clusters. This is because in genuine unsupervised learning problem, we have no idea how many clusters we should look for! Also, in my view, this clever clustering technique solves some ambiguity issues regarding vague definition of a cluster and thus is more than suitable for automatic detection of such structures. On the other hand, the agglomerative clustering process employs standard metrics for clustering quality description. Therefore, it will be fairly easy to observe what is going on. Continue reading
When building predictive models, you obviously need to pay close attention to their performance. That is essentially what it is all about – getting the prediction right. Especially if you are working for paying clients you need to prove that the performance of your models is good enough for their business. Fortunately, there is a whole bunch of statistical metrics and tools at hand for assessing model’s performance.
In my experience, performance metrics for (especially binary) classification tasks such as confusion matrix and derived metrics are naturally understood by almost anyone. A bit more problematic is the situation for regression and time series. For example when you want to predict future sales or want to derive income from other parameters, you need to show how close your prediction is to the observed reality.
I will not write about (adjusted) R-squared, F-test and other statistical measures. Instead, I want to focus on performance metrics that should represent more intuitive concept of performance as I believe they can help you to sell your work much more. These are:
- mean absolute error
- median absolute deviation
- root mean squared error
- mean absolute percentage error
- mean percentage error