Reinforcement Learning (RL) is an area of Machine Learning where the model is trained to make a sequence of decisions under different conditions. Let’s see an example: Let’s imagine that we have a robot vacuum that cleans the floor in the apartment. For the robot to have an

Feature engineering is the process of transforming your input data in such a way that it will be more representative of the Machine Learning Algorithms. However, it is very often forgotten because of the inexistence of an easy-to-use package. That’s why we decided to create the one — imperio, the

Feature engineering is the process of transforming your input data in such a way that it will be more representative of the Machine Learning Algorithms. However, it is very often forgotten because of the inexistence of an easy-to-use package. That’s why we decided to create the one — imperio, the

Feature engineering is the process of transforming your input data in such a way that it will be more representative of the Machine Learning Algorithms. However, it is very often forgotten because of the inexistence of an easy-to-use package. That’s why we decided to create the one — imperio, the

Feature engineering is the process of transforming your input data in such a way that it will be more representative of the Machine Learning Algorithms. However, it is very often forgotten because of the inexistence of an easy-to-use package. That’s why we decided to create the one — imperio, the

Unbalanced data In simple terms, an unbalanced dataset is one in which the target variable has more observations in one specific class than the others. An example can be a dataset for transactions: usually, 99% of this set consists of simple, not fraudulent transactions, and only 1% of them are

Unbalanced data In simple terms, an unbalanced dataset is one in which the target variable has more observations in one specific class than the others. An example can be a dataset for transactions: usually, 99% of this set consists of simple, not fraudulent transactions, and only 1% of them are

Unbalanced data In simple terms, an unbalanced dataset is where the target variable has more observations in one specific class than the others. An example can be a dataset for transactions: usually, 99% of this set consists of simple, not fraudulent transactions, and only 1% of them are fraud. What

This article will be about a combination of the two most powerful algorithms used for oversampling and undersampling imbalanced datasets, SMOTE and ENN. I talked about SMOTE in one of my previous articles, so you should check it out because I will now rely more on the ENN algorithm. How

This article will be about a combination of the two most powerful algorithms used for oversampling and undersampling imbalanced datasets, SMOTE and Tomek Links. I talked about SMOTE in my previous article, so you should check it out because I will now rely more on the Tomek Links algorithm. So

This will be a new series of articles related to our brand new Python library made with love by Sigmoid. Its name is just like the second unforgivable spell from the Harry Potter series, Crucio. This library was created specifically for unbalanced data sets, with a lot of different methods

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

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