Explorium Blog

  • Understanding and Handling Data and Concept Drift

    Understanding and Handling Data and Concept Drift

    Over time, ML models start to lose predictive power due to a concept known as model drift. How can you spot data and concept drift and avoid it? Read more.

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  • Stay Inside The Lines: Coloring With Artificial Intelligence

    Stay Inside The Lines: Coloring With Artificial Intelligence

    Can you teach AI to color better than a human? Using a generative adversarial network we can certainly try.

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  • Support and Coverage – Data Integration Metrics You Should Know

    Support and Coverage – Data Integration Metrics You Should Know

    Data enrichment is a crucial step in the modeling process that data scientists tend to overlook due to the difficulty in finding and utilizing external sources.

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  • Clustering — When You Should Use it and Avoid It

    Clustering — When You Should Use it and Avoid It

    Cluster analysis is an essential tool for data scientists but it shouldn’t be your only one. Discover when you should, and shouldn’t, use clustering.

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  • AI Across the Funnel: The Use Cases You’re Missing Out On

    AI Across the Funnel: The Use Cases You’re Missing Out On

    Building core operations around ML models has helped many companies thrive across many other verticals. Still, others wait to implement at a large scale. Why?

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  • It’s Official: Online Lenders Can No Longer Afford to Ignore Alternative Data

    It’s Official: Online Lenders Can No Longer Afford to Ignore Alternative Data

    You don’t know what you don’t know, as the old saying goes. But in the age of Big Data, you simply can’t afford to shrug off "unknown unknowns".

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  • The Top Six Must-Attend Data Science Conferences Coming in 2020

    The Top Six Must-Attend Data Science Conferences Coming in 2020

    These data science conferences will ensure your 2020 is packed with lots of learning and networking opportunities.

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  • Cracking The Stability of Feature Selection

    Cracking The Stability of Feature Selection

    Some feature selection strategies may perform well when created but tend to break when tested later, meaning that some features are unstable and may perform badly on new data.

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  • 5 Steps to Mine Hidden Gold Out of An External Data Source

    5 Steps to Mine Hidden Gold Out of An External Data Source

    Extracting gold from an external data source is all about data prep. Follow these five steps and you’ll be well on your way to creating golden enrichments.

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  • Extend Your Machine Learning Pipeline With Your Prediction Outcome

    Extend Your Machine Learning Pipeline With Your Prediction Outcome

    Does your machine learning pipeline end after your model makes a prediction? If your answer is “yes,” it doesn’t have to be.

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  • 2020: The Year Data Dominates Data Science

    2020: The Year Data Dominates Data Science

    From data economy growth to KPI-driven DS teams, our data science predictions for 2020 are all about the data.

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  • The Complete Guide to Decision Trees

    The Complete Guide to Decision Trees

    Decision trees are one of the most popular algorithms out there but how much do you really know about them? This technical deep-dive breaks down everything you need to know.

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  • Explorium secures $19M funding to automate data science and machine learning-driven insights

    Explorium secures $19M funding to automate data science and machine learning-driven insights

    Part ML platform, part data marketplace, Explorium promises to automate data and feature discovery, and build and deploy models for your analytics and application needs.

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  • Why Automating Data Science Will Kill the BI Industry

    Why Automating Data Science Will Kill the BI Industry

    Machine learning models, unlike business intelligence, can infer more complex rules, deeper patterns, and interactions between different dimensions and different variables.

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  • Challenges in Maintaining Real-Time ML Models Using Multiple Data Sources

    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 The post Challenges in Maintaining Real-Time ML...

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  • Using Data Science to Predict the Next Hit Song (Part 2)

    Using Data Science to Predict the Next Hit Song (Part 2)

    Part two of our hit song classifier uses data enrichments from genius.com for sentiment analysis and to increase our model's performance.

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  • Beginner’s Guide to Python Modeling Using XGBoost Package

    Beginner’s Guide to Python Modeling Using XGBoost Package

    It's vital that banks retain their customers and prevent churn. How can they predict if a customer will leave? We walk you through an ML algorithm that can help.

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  • Using Data Science to Predict the Next Hit Song (Part 1)

    Using Data Science to Predict the Next Hit Song (Part 1)

    Can data science really predict the next hit song? We built a hit song classifier enriched with data to try and find out.

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  • Top 10 Evaluation Metrics for Classification Models

    Top 10 Evaluation Metrics for Classification Models

    Are your models dependable? Are you tracking the right metrics for your needs? Let’s find out.

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  • Demystifying Feature Selection: Filter vs Wrapper Methods

    Demystifying Feature Selection: Filter vs Wrapper Methods

    Feature selection algorithms are increasingly growing in importance. In this article, we will cover (and compare) two popular feature selection methodologies: filter and wrapper.

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