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decision tree
December 10, 2019 Explorium Data Science Team Data Science

The Complete Guide to Decision Trees

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

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Real Time Models
November 27, 2019 Juan De Dios Santos Machine Learning

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

Hit Song Classifier Part 2
November 20, 2019 Maël Fabien Data Science

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

Predict customer churn
November 11, 2019 Explorium Data Science Team Data Science

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

Data Science to Predict the Next Hit Song
November 6, 2019 Maël Fabien Data Science

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

October 23, 2019 Eilon Baer Predictive Models

Top 10 Evaluation Metrics for Classification Models

It’s important to understand that none of the following metrics are an absolute measure of your machine learning model’s accuracy. However, when measured in tandem with

August 11, 2019 Aviv Nutovitz Data Science

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.

July 24, 2019 Maël Fabien Data Science

Interpretability and explainability (Part 2)

The whole idea behind interpretable and explainable ML is to avoid the black box effect.

July 15, 2019 Maël Fabien Data Science

Interpretability and explainability (Part 1)

The whole idea behind interpretable and explainable ML is to avoid the black box effect.

Categories
  • All posts
  • Automation
  • Business Intelligence
  • Data Enrichment
  • Data Science
  • Explainability
  • Feature Engineering
  • Feature Selection
  • General
  • Interpretability
  • Machine Learning
  • Predictive Models
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