Introduction Why Dask? For those of us who regularly work with python machine learning libraries, the Pandas DataFrame library is a firm fixture in our toolkit. Pandas DataFrames allow fast and efficient manipulation of data and a host of data wrangling functions. And the processing is fast because pandas does
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Introduction This is the next article in my collection of blogs on anomaly detection. This time we will be taking a look at unsupervised learning using the Isolation Forest algorithm for outlier detection. I’ve mentioned this before, but this time we will look at some of the details more closely.
Read moreWe can all think of examples of successful forecasting, but what should we be aware of and what pointers can we adopt going forward?
Read moreNot all models need to be pre-trained. Sometimes it is more effective to apply algorithms inline to small batches of data.
Read moreThe M-competitions compare and evaluate different approaches to, and implementations of, time-series forecasting. Here is a brief review of the latest one, M5
Read moreWe must give consideration to the languages used in model training and model deployment – and we should do this before any model work begins. It is better to consider the two environments (and sometimes the two teams) as a whole and then work to a common interface.
Read moreIntroduction Unsupervised anomaly detection with unlabeled data – is it possible to detect outliers when all we have is a set of uncommented, context-free signals? The short answer is, yes – this is the essence of how one deals with network intrusion, fraud, and other types of low-instance anomaly. In
Read moreTime-to-event (TTE) use-cases crop up in many places across industries. Some examples would be: the prediction of customer churn (the sales domain), remaining-useful-life or time-to-failure TTF (predictive maintenance), or anomaly detection (machine monitoring). Some events are difficult to predict as they are hidden. We can instead try to look for interim events to improve prediction accuracy.
Read moreTime-to-event (TTE) use-cases crop up in many places across industries. Some examples would be: the prediction of customer churn (the sales domain), remaining-useful-life or time-to-failure TTF (predictive maintenance), or anomaly detection (machine monitoring).Some events are difficult to predict as they are hidden. We can instead try to look for interim events to improve prediction accuracy.
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