Gustavo

Gustavo Dudamel and the Los Angeles Philharmonic have earned a Grammy Award nod for best orchestral performance alongside another L.A. nominee, conductor Christopher Rountree’s experimental new music chamber orchestra, Wild Up. The two groups are up against conductor Doug Perkins and the Musicians of the University of Michigan Department of Chamber Music and the University of Michigan Percussion Ensemble; John Williams and the Berliner Philharmoniker; and Michael Repper and the New York Youth Symphony.Dudamel and the L.A.…

Data cleaning is necessary. I believe most of us agree on that. A project will usually begin with some exploration and cleaning before we can go to the modeling part.Actually, I would say that most of a Data Scientist’s work is done between cleaning and transforming the dataset.The problem to be solved in this quick tutorial is for us to deal with variable encoding. Most of the machine learning algorithms expect numbers instead of text to estimate something. After all, computers are logical machines that rely on numbers…

Outliers are a frequently discussed subject in many Data Science forums and blogs. This is probably because these data points can distort our analysis and can affect the modeling, if the algorithm we’re using is not robust to those anomalies.A dataset, many times, will bring the majority of the observations within a certain range of values, following some patterns, staying not too far from the “group”. These are the inliers. But there will be also those observations that won’t fit anywhere, that are far away from the…

If you ever faced a dataset with, let’s say, 100 features, you probably thought about reducing the number of dimensions of it.First, because it is really difficult for us to create compelling visualizations having that many attributes. It is really difficult to know what really matters to plot.Second, because there’s a concept known as Curse of Dimensionality that tells that a dataset with too many variables tend to become sparse (too many zeroes) and that makes it more difficult for machine learning models to estimate…

In Data Science, there are many algorithms available for use these days. One useful technique, therefore, is to use combine them in a single model to get the best out of each, resulting in a more accurate model.Using Scikit-Learn, you will find Random Forest algorithm, that is the bagging kind of ensemble model. On the other hand, you will also find Boosting models, that train the estimators in sequence, where the result of one model is passed to the next one, that will try to improve the predictions, until they reach an…

The Punjabi hip-hop scene in Brampton is getting louder every day. Rapper Varinder Brar teamed up with fellow Brampton native Gustavo Guaapo on their celebratory new single “Mashook.”The single, in which the title translates to “beloved,” sees Brar and Guaapo rapping their respective verses in Punjabi and English while riding around their city. Despite the different languages, their chemistry runs high across the three-minute track.The accompanying video cuts between the two artists dancing in a parking lot and racing…

Classification ensemble models are those composed by many models fitted to the same data, where the result for the classification can be the majority’s vote, an average of the results, or the best performing model result.Figure 1: Ensemble model with voting result. Image by the author.In Figure 1, there is an example of the voting classifier that we are going to build in this quick tutorial. Observe that there are three models fitted to the data. Two of them classified the data as 1, while one classified as 0. So, by the…

The good old linear regression is a widely used statistical tool to determine the linear relationship between two variables, enabling the analysts to make inferences and extract good insights from the data, including predictions.However, there are not only linear data. Not all datasets carry a linear pattern. There are cases that they’re almost there, but we need to make transformations to “help” them fitting in a linear algorithm.One of the possibilities is the power transformation, making a quadratic or cubic equation,…

Learn how to choose between SciKit-Learn’s GridSearchCV or RandomizedSearchCVPhoto by Markus Winkler on UnsplashMachine learning is not always straightforward as in Iris, Titanic or Boston House Pricing datasets.But, hey, don’t get me wrong. I learned (and keep learning) a lot from those famous toy datasets. The great advantage of them is that they don’t require too much of exploration or preprocessing. Many times, we can go right to the point we want to practice and learn, like pipelines, modeling, model tuning,…

Shapley Additive Explanations for Python can help you to easily explain how a model predicts the resultPhoto by Sam Moghadam Khamseh on UnsplashComplex machine learning models are constantly referred to as “black boxes”. Here is a good explanation for the concept.In science, computing, and engineering, a black box is a device, system, or object which produces useful information without revealing any information about its internal workings. The explanations for its conclusions remain opaque or “black”. (Investopedia)So,…