One of the most intuitive ways to select features would be to find how much the distribution of the classes is different from each other. However on some intervals, the distribution of the feature by the classes can be different, but on other intervals, it can be practically the same.

Principal Component Analysis is known as one of the most popular dimension reduction techniques. However few know that it has a very interesting property — the reduced data can be brought back to the original dimension. Even more, the data brought back to its original size is more cleaned. So, at

Even if we don’t leave in the world of Harry Potter, now everyone can learn the unforgivable spells to use some not easy algorithms just by typing 3 lines of code. This can be done by using the Kydavra library for python. Using Kydavra ElasticNetSelector. To install Kydavra we

Many times, we have some features that are strongly correlated with the target column. However, sometimes they are correlated with each other, generating in such a way the problem of multicollinearity. One way is to reduce one of these columns. But, we at sigmoid want to propose to you a

Usually, when we try to select the best features of the dataset, we choose different spells(algorithms) to see a difference, and select the best one, so why don’t we try to mix them all up and combine the power them all in one spell. This is where the

Very often we can look at classification as a problem of finding differences between 2 groups. Before Machine Learning Statisticians were doing it quite a lot. Mostly they used such metrics as mean, variation, and standard deviation. However, it was a time-consuming process, and couldn’t be done with many

Features selection is a very important part of machine learning development, It allows you to keep your models as simple as possible keeping at the same time, as much information as possible. Unfortunately, sometimes it can require high mathematical knowledge and good practical skills in programming. However, at Sigmoid we

We all know the Occam’s Razor: From a set of solutions took the one that is the simplest. This principle is applied in the regularization of the linear models in Machine Learning. L1-regularisation (also known as LASSO) tend to shrink wights of the linear model to 0 while L2-regularisation

So how we said in previous articles about Kydavra library, Feature selection is a very important part of Machine Learning model development. Unfortunately, there is not only one unique way to get the ideal model, mostly because of the fact that data almost every time has different forms, but this

The majority of machine learning problems in today’s world are the classification ones. Mostly data scientists and machine learning engineers use Correlations as Pearson or Spearman to find features that correlate the most with the predicted value. However, these types of correlations work better on continuous-continuous pair of features.

From the rising of the Machine Learning and Artificial Intelligence fields Probability Theory was a powerful tool, that allowed us to handle uncertainty in a lot of applications, from classification to forecasting tasks. Today I would like to talk with you more about the use of Probability and Gaussian distribution

Almost every person in data science or Machine Learning knows that one of the easiest ways to find relevant features for predicted value y is to find the features that are most correlated with y. However few (if not a mathematician) know that there are many types of correlation. In

The development of machine learning implies a lot of maths. But sometimes during feature selection phase maths sometimes can’t give an exact answer (because of the structure of data, it’s source, and many other causes). Then in-game enter the Programming tricks, mostly brute force methods :). Genetic algorithms are

Nowadays, AI-related fields like Data Science and Machine Learning have become mainstream. Practically everyone can now become a data scientist. That’s, of course, a good thing, but unfortunately, people forget about what an AI system is. I prefer the notion by Patrick Winston from MIT: Artificial Intelligence (or Intelligent