Some flaws of Principal Component Analysis that affect supervised machine learning projects — Principal Component Analysis is a very useful dimensionality reduction tool. It can really help you reduce the number of features of a model. Although it may seem a powerful tool for a data scientist, there are some drawbacks that I think make it unsuitable for supervised machine learning projects. What is PCA?

Let’s compare our model with a trivial baseline — Every time we train a model we should check if its performance beats some baseline, which is a trivial model that doesn’t take the inputs into account. Comparing our model with a baseline model, we can actually figure out whether it actually learns or not. What is a baseline model? A baseline model is a…

Some techniques to see if a dataset is imbalanced — Dealing with imbalanced datasets is always hard for a data scientist. Such datasets can create trouble for our machine learning models if we don’t deal with them properly. So, measuring how much our dataset is imbalanced is important before taking the proper precautions. …

Tips and techniques to decide whether it is time to retrain our model — Training a model is a complex process requiring much effort and analysis. Once a model is ready, we know that it won’t be valid forever and that we’ll need to train it again. How can we decide if a model needs to be retrained? …

A brief overview about models that need pre-processed data — Data pre-processing is an important part of every machine learning project. A very useful transformation to be applied to data is normalization. Some models require it as mandatory to work properly. Let’s see some of them. What is normalization? Normalization is a general term related to the scaling of the variables. Scaling transforms…

A brief overview of some interpretable machine learning models — Model explanation is an essential task in supervised machine learning. Explaining how a model can represent the information is crucial to understanding the dynamics that rule our data. Let’s see some models that are easy to interpret. Why do we need to interpret our models? Data Scientists have the role to extract information from raw data. They aren’t…

A simple guide on how to use statistics to run A/B tests on proportions — Online marketing and startup growth are better if you can continuously test different ideas. The statistic comes into help when we have to perform A/B tests. The results you may achieve with the proper analysis can give your project a great boost. What is an A/B test? Companies often need to compare the results of…

A short guide on how to create comparable training and test datasets — Data scientists usually split a dataset into training and test sets. Their model is trained on the former and then its performance is checked in the latter. But, if these sets are sampled wrongly, model performance may be affected by biases. Should training and test sets be similar? From the beginning of my career, everybody used to…

How to create Node.js or client-side models that aren’t too CPU-expensive—while still making the most of Python’s libraries — Models are very powerful tools to be used in several web applications. The general approach is to make them accessible by using REST APIs. In this article, I’ll talk about a model that can be created in Python and then deployed in pure Javascript. Why Javascript? Machine learning models are often created…