Learn how to explain machine learning models with LIME. LIME stands for Local Interpretable Model-Agnostic Explanations and is used to understand which model features have the most predictive impact.

This article uses a Kaggle competition as an opportunity to show how data science can be used in digital marketing to answer a specific question, and take what is learned from the data and apply it to marketing strategies.

In Data Science With R Course Series - Week 6, learn how to analyze model performance and communicate machine learning model results to stakeholders.

The culmination of the previous weeks has been preparation for machine learning modeling. At this stage, we have defined the business problem, we’ve explored and understood how the data relates to the business problem, and we’ve preprocessed the data in preparation for modeling.

This week in the Data Science With R Course Series we’ll cover Data Preparation, where we structure the data in preparation for modeling. This week’s modules will teach you:

R and Python - learn how to integrate both R and Python into your data science workflow. Use the strengths of the two dominant data science languages.

Data Science and Machine Learning in business begins with R. Why? R is the premier language that enables rapid exploration, modeling, and communication in a way that no other programming language can match: SPEED! This is why you need to learn R. Time is money, and, in a world where you are measured on productivity and skill, R is your machine-learning powered productivity booster.

Data Science and Machine Learning in business begins with R. Why? R is the premier language that enables rapid exploration, modeling, and communication in a way that no other programming language can match: SPEED! This is why you need to learn R. Time is money, and, in a world where you are measured on productivity and skill, R is your machine-learning powered productivity booster.

Data Science and Machine Learning in business begins with R. Why? R is the premier language that enables rapid exploration, modeling, and communication in a way that no other programming language can match: SPEED! This is why you need to learn R. Time is money, and, in a world where you are measured on productivity and skill, R is your best friend.

Artificial intelligence (AI) has the potential to change industries across the board, yet few organizations are able to capture its value and realize a real return-on-investment. The reality is that the transition to AI and data driven analysis is difficult and not well understood. The issue is twofold, first, the necessary technology to complete such a task has only recently become mainstream, and second, most data scientists are inexperienced in their respective industries. However, with all the uncertainty surrounding this topic, one hedge fund has managed to navigate through these challenges and accomplish what many companies are failing to do: building a high-performing data science team that achieves real return-on-investment (ROI).

Data Science is an amazing field of research that is under active development both from the academia and the industry. One of the saddest facts in the real-world is that most data science projects in organizations fail. Here I’ll present a new iteration of an agile framework called Business Science Problem Framework (Download PDF here) to implement data science in a way that enables decision making to follow a systematic process that connects the models you create to Return On Investment (ROI) and show the value that your improvements bring to the business. The end result is that the BSPF is an agile framework, and we are working to develop a new visualization (BSPF 2.0) that conveys this agility.

Model interpretability is critical to businesses. If you want to use high performance models (GLM, RF, GBM, Deep Learning, H2O, Keras, xgboost, etc), you need to learn how to explain them. With machine learning interpretability growing in importance, several R packages designed to provide this capability are gaining in popularity. We analyze the IML package in this article.