How likely is it that the best player is going to win — Last day, I came across a post talking about how a slight percentage of probability of winning a point can get bigger in the probability of winning a match. At first, it seems like magic but thinking about the cascading multiplication of probabilities you can see that it’s logical. …

An introduction to information theory — Introduction The universe is overflowing with information. Everything we say, hear, think and see is an information. Everything wraps an information in it, that must follow a certain rule no matter the format. But sometimes we want to process these information, whether to compare it to another or to quantify how…

The complete guide to understand covariance, correlation and R² — In the last article we talked deeply about variance and standard deviation and how they will briefly summarize all features for a given data. They are the first impression for a given variable, to see how it is distributed in our sample. Based on those, today we are going to…

Understanding what’s behind the bank rate. — Last day, I came across a post talking about how someone who took a mortgage loan is now suffering to repay his monthly payments because it’s been 15 years now and he didn’t understand the first time the rate he had. And I had this question in my mind: What…

The complete guide to understand variance and standard deviation — Descriptive statistics are used to describe the basic information about the data. They help us understand some features of the data by giving short summaries about the sample. It’s like the first impression of what the data shows us. In a nutshell, descriptive statistics are metrics and quantitative analysis to…

Understanding the confusion matrix, AUC and ROC curve with their implementations — Machine learning classification metrics are not that hard to think about if the data are quite clean, neat and balanced. We can just compute the accuracy with the division of the true predicted observations by the total observation. This is not the case in general. In fact, a lot of…

Learn PCA with its interpretation and its implementation in R — Principal Component Analysis (PCA) is a method of dimensionality reduction, it can be used for feature extraction or representation learning. It transforms the data from a d-dimensional space into a new coordinate system of p dimensions(p≤d), and extracting the most important q variables(q << d) When should I use it ? First of all we need…

Learn how logistic regression is implemented from scratch — Logistic regression is a classification algorithm, which is pretty popular in some communities especially in the field of biostatistics, bioinformatics and credit scoring. It’s used to assign observations a discrete set of classes(target). Why not logistic classification? Logistic regression is strictly not a classification algorithm on its own. Because its output is a probability(a…