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A popular phrase tossed around when we talk about statistical data is “there is correlation between variables”. However, many people wrongly consider this to be the equivalent of “there is causation between variables”. It’s important to explain the distinction: Correlation means that once we know how one variable changes we can make reasonable deductions aboutRead more

One of the most typical tasks in machine learning is classification tasks. It may seem that evaluating the effectiveness of such a model is easy. Let’s assume that we have a model which, based on historical data, calculates if a client will pay back credit obligations. We evaluate 100 bank customers and our model correctlyRead more

In the previous post of our Understanding machine learning series, we presented how machines learn through multiple experiences. We also explained how, in some cases, human beings are much better at interpreting data than machines. In many tasks machines still can’t replace humans, who understand surrounding reality better and can make more accurate decisions. MachinesRead more

“If (there) was one thing all people took for granted, (it) was conviction that if you feed honest figures into a computer, honest figures (will) come out. Never doubted it myself till I met a computer with a sense of humor.” ― Robert A. Heinlein, The Moon is a Harsh Mistress   This post isRead more

In the previous parts of our tutorial we discussed: Basic notation used in assessing classification models Quantitative quality indicators Confusion Matrix In this fourth part of the tutorial we will discuss the ROC curve. What is the ROC curve? The ROC curve is one of the methods for visualizing classification quality, which shows the dependencyRead more

In the previous parts of the tutorial (part 1, part 2) we introduced quantitative indicators of classification model quality. In the next two parts we will take a closer look at a couple of graphical indicators. The first one is called the Confusion Matrix (the name “Contingency Table” is also used). What is a ConfusionRead more