Explorium's Data Insights Blog
Where data, marketing, and sales professionals come together
2020: The Year Data Dominates Data Science
Data has been a hot topic of discussion for years now. As organizations create and collect more and more data, it’s key that they use it to make actionable decisions and open up new lines of business. However, most organizations are only scratching the surface of what their data can do — typically using it […]
The Complete Guide to Decision Tree Analysis
In the world of machine learning, developers can create independent environments for projects easily. It only takes a few clicks to set and fit models in order to achieve solid results. Yet, many algorithms can be quite difficult to understand, let alone explain. Decision trees, while performing poorly in their basic form, are easy to […]
Challenges in Maintaining Real-Time ML Models Using Multiple Data Sources
The field of machine learning is currently experiencing a rapid and booming expansion that seems to have no end. It seems like every other day, the ML community publishes a new and exciting paper that shows the latest state-of-the-art algorithm or software for a use case that two weeks ago had a not-so-state-of-the-art solution. These […]
Using Data Science to Predict the Next Hit Song (Part 2)
In part one of this two-part series, we explored basic models and data enrichments for our hit song classifier. In this article, we will try to push our model a little more by attempting to improve its performance through better data exploration, enrichment and feature engineering. Before we get started, let’s recall the context. The […]
Beginner’s Guide to Python Modeling Using XGBoost Package
Everyone knows that having a large number of loyal customers is the key to the success of a business. This statement is even more significant for banks. It is important for banks to retain their customers and prevent churn — the term used to describe when a customer ends their contract or business with an […]
Using Data Science to Predict the Next Hit Song (Part 1)
It comes as no surprise that the music industry is tough. When you decide to produce an artist or invest in a marketing campaign for a song there are many factors to take into account. What if data science could help with this task? What if data science could predict whether a song is going […]
Top 10 Evaluation Metrics for Classification Models
It’s important to understand that none of the following evaluation metrics for classification are an absolute measure of your machine learning model’s accuracy. However, when measured in tandem with sufficient frequency, they can help monitor and assess the situation for appropriate fine-tuning and optimization. Here are a few values that will reappear all along this […]
Demystifying Feature Selection: Filter vs Wrapper Methods
Feature selection algorithms are increasingly growing in significance. In this article, we will cover (and compare) two popular feature selection methodologies – Filter and Wrapper.
Interpretability and explainability (Part 2)
The whole idea behind interpretable and explainable ML is to avoid the black box effect.
Interpretability and explainability (Part 1)
The whole idea behind interpretable and explainable ML is to avoid the black box effect.
The ultimate machine learning model deployment checklist
While there is some room for error while integrating models into production environments, there is also a very good probability that these issues will eventually lead to disaster. And that’s exactly why we have created this pre-model deployment checklist.
Who’s the painter?
Better features, better data
The spectrum of complexity
Demystifying the old battle between transparent, explainable models and more accurate, complex models.
Why automating data science will kill the BI industry
Machine learning models are mathematical models that leverage historical data to uncover patterns which can help predict the future to a certain degree of accuracy. And when it comes to running a business, the ability to predict and make data-driven decisions (from the ability to identify customer churn before it happens, to optimizing promotions and […]
