For the last decades, lots of efforts have been put in developing machine learning algorithms and methods. Those methods are currently being widely used among companies and let us extract meaningful insights from our raw data to solve complex problems that could hardly be solved otherwise. They make our life (and our job) easier, but at what cost? There is a good reason why Machine learning methods are known as being “black-box”: They have turned so complex that is hard to know what is exactly going on inside them. However, understanding how models work and making sure our predictions make any sense is an important issue in any business environment. We need to trust our model and our predictions in order to apply them for business decisions. Understanding the model also help us debug it, potentially detect bias, data leakage and wrong behaviour. Towards interpretability: The importance of knowing our data We should take into account that, whenever we talk about modelling, there needs to be a lot of work behind related to data preparation and understanding. Starting with the clients’ needs or interest, those need to be translated into a proper business question, upon which we will then design an experiment. That design should specify, not just the desired output and the proper model to use for it, but also – and more important – the data needed for it. That data needs to exist, be queried and have enough quality to be used. Of course, data also needs to be explored and useful variables (i.e. variables related to the output of interest) be selected. In other words: Modelling is not an isolated process and its results cannot be understood without first understanding the data that has been used to get those results, as well as its relationship with the predicted outcome. Interpretable vs. non interpretable models Until now, we have just talked about black-box models. But are actually all models hard to interpret? The answer is no. Some models are simpler and intrinsically interpretable, including linear models and decision trees. But since that decrease in complexity comes with a cost on the performance, we usually tend to use more complex models, which are hardly interpretable. Or are they? Actually, intensive research has been put into developing model interpretability methods and two main type of methods exist:
Those model methods can also be grouped, depending on their predictions scope, into:
Some Global interpretability examples As previously mentioned, probably the most widely method used is the calculation of the feature importance, and many packages have their own functions to calculate it. For instance, package caret has the function varImp(), which we have used to plot the following example. There, we can see how feature “gender-male” and “age” seem to be the most important features to predict the survival probability in the titanic (yes! we have used the famous Kaggle titanic-dataset to build our models). ![]() Partial dependence plots are also widely used. These plots show how predicted output changes when we change the values on a given predictor variable. In other words, it shows the effect of single features on the predicted outcome, controlling for the values of all other features. In order to build them, function partial() from package pdp can be used. For instance, in the following partial depende plot we can see how paying a low fare seems to have a positive effect on the survival – which makes sense, knowing for instance that children had preference on the boats! Some local interpretability examples Local interpretability techniques can be studied with the packages DALEX and modelStudio, which let us use a very nice and interactive dashboard – where we can choose which methods and which observations are we most interested at. ![]() One of the best methods contained are the so-called break-down plots, which show how the contributions attributed to individual explanatory variables change the mean model prediction to yield the actual prediction for a particular single observation. In the following example of a 30 year old male travelling on 2nd class, which payed 24 pounds and boarded in Cherbourg, we can see how the boarding port and the age had a positive contribution on the survival prediction, whereas his gender and the class had a negative one. In this way, we can study each of the observations which we want or have to focus on – for instance, if we think that the model is not working properly on them. Shap values is a similar method, which consists on taking each feature and testing the accuracy of every combination of the rest of features, checking then how adding that feature on each combination improves the accuracy of the prediction. On the following example, and for the same observation as we just analysed, we can see that result are very similar: gender shows the biggest and most negative contribution, while the boarding port has the biggest and most positive effect on the survival prediction, for that specific passenger. Last, if we are interested on how observations’ predictions change when changing feature values, we can study the individual conditional expectation plots. Even though they can just display one feature at a time, it let us have a feeling on how predictions change when feature values change. For instance, on the following example we can see how increasing the age have a negative effect on the survival of the titanic passengers. Some last words
In this post, we have made a brief introduction on the interpretability of machine learning models, we have explained why is important to actually be able to interpret our results and we have shown some of the most used methods. But just as a reminder: for a similar performance, we should actually always prefer simpler models which are interpretable per se, over super complex machine learning ones!
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Cognitive biases are systematic patterns of deviation from rationality in judgment. These biases are subject to research interests in fields like psychology and behavioral economics. What we call cognitive biases are mechanisms that have developed within an evolutionary process. They already helped our ancestors in making fast decisions when needed and with limited information processing capabilities. These biases are not only an essential building block of our "gut feeling" but also our intuition to a ceratin degree. This is what Daniel Kahnemann, nobel prize winner for economics in 2002, has called System 1, the area of unconscious and fast decision making in our minds. The speed and ease of this sytem comes with a price as biases can lead to irrational and counter-factual decisons. Biases can affect human power of judgment in a professional context and in personal life. Presumably rational and fact-oriented people like analysts and data scientists are not save from cognitive biases either. Some authors even argue that they are even more prone to be to biased due to the experimental and research-oriented nature of their work. As biases are essentially part of human nature and they are everywhere, it is important to be aware of them. This might enable us to give better advice to others and take more informed decisions ourselves. We will try to provide a light introdcution, some hints for prevention and some interesting sources for further reading. Let us look at the most relevant cognitive biases one by one.
So what?You can get it if you really want. But you must try, try and try. Jimmy Cliff, You can get if you really want. Overcoming cognitive biases completely might be almost impossible. However, raised awareness of how our minds try to trick us will already lead to noticeable improvements in judgment. If you are interested in the topic, we can recommend the following readings. Books
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Released: 2011
Big names: Brad Pitt, Philip Seymour Hoffman
IMDB Rating: 76%
Plot in a nutshell: The movie is based on the book Moneyball: The Art of Winning an Unfair Game by Michael Lewis. Its main protagonist is Billy Beane who started as General Manager of the baseball club Oakland Athletics in 1997. Beane was confronted with the challenge of building a team with very limited financial resources and introduced predictive modelling and data-driven decision making to assess the performance and potential of players. Beane and his peers were successful and managed to reach the playoffs of the Major Leage Baseball several times in a row.
Trailer:
Released: 2014
Big names: Benedict Cumberbatch, Keira Knightley
IMDB Rating: 80%
Plot in a nutshell: The Imitation Game is based upon the real-life story of British mathematician Alan Turing who is known as the father of modern computer science and for the test named after him. The film is centered around Turing and his team of code-breakers working hard to decipher the Nazi German military encryption Enigma. To crack the code, Turing creates a primitive computer system that would consider permutations at a much faster speed than any human could. The code breakers at Bletchley Park succeeded and thereby not only helped Allied forces ensure victory over the Wehrmacht but contributed to shorten the horros of the Second World War.
Trailer:
Released: 2011
Big names: Paul Bettany, Stanley Tucci, Demi Moore
IMDB Rating: 71%
Plot in a nutshell: Margin Call plays during the first days of the last global financial crisis in 2008. A junior analyst at a large Wall Street investment bank discovers a major flaw in the risk evaluation model of the bank. The story develops during the night as the young employee informs senior managers that the bank is close to a financial disaster, knowing that the bancruptcy of the firm would lead to a dramatic chain reaction in the market – and millions of lives would be affected.
Trailer:
Released: 2008
Big names: Kate Bosworth, Laurence Fishburne
IMDB Rating: 68%
Plot in a nutshell: Six students of the renowned Massachusetts Institute of Technology (MIT) get trained in card counting and rip off Las Vergas casinos at various blackjack tables. The film is based upon a true story.
Trailer:
We hope our tipps are valuable for you and you enjoy any of the flicks. 📺🎬 🍿☕🍷
Take care and all the beston behalf of joint systems
Johannes

Particularly in uncertain times like these, organizations strive to predict the future in the best possible way. Previously we have already explored multiple times how to forecast future income using the past income trajectory, for instance in these blog posts. We now want to go a step further and investigate the relationship between fundraising income and the general economic climate, exploring whether or not it is possible to infer extra information from and improve income forecasting tools by using economic indicators.
Introduction
Granger causality
Cointegration
Vector autoregression (VAR)
ARIMA and ARIMAX
We have used all four time series to construct an ARIMAX-model, using the economic data to help forecast the amount of sporadic donations. Again we used the data until 2016 as our training set, with the data from 2017 to 2020 as a test set to evaluate results. We have also used a standard ARIMA-model to construct a forecast for sporadic donations only on the time series’ historical data. Interestingly, the models’ projected forecasts did not differ much from each other:
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