Blog Archives

Explaining Predictions: Boosted Trees Post-hoc Analysis (Xgboost)

October 11, 2019
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Explaining Predictions: Boosted Trees Post-hoc Analysis (Xgboost)

Recap We’ve covered various approaches in explaining model predictions globally. Today we will learn about another model specific post hoc analysis. We will learn to understand the workings of gradient boosting predictions. Like past posts, the Clevaland heart dataset as well as tidymodels principle will be used. Refer to the first post of this series for more details. Gradient Boosting Besides random forest...

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Explaining Predictions: Random Forest Post-hoc Analysis (randomForestExplainer package)

August 30, 2019
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Explaining Predictions: Random Forest Post-hoc Analysis (randomForestExplainer   package)

Recap This is a continuation on the explanation of machine learning model predictions. Specifically, random forest models. We can depend on the random forest package itself to explain predictions based on impurity importance or permutation importance. Today, we will explore external packages which aid in explaining random forest predictions. External packages There are external a few packages which offer to calculate variable...

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Explaining Predictions: Random Forest Post-hoc Analysis (permutation & impurity variable importance)

July 27, 2019
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Intro Recap There are 2 approaches to explaining models Use simple interpretable models. This approach was covered in the previous posts where we looked at logistic regression and decision trees as examples of white box models. Conduct post-hoc interpretation on models. There are two are two types of post-hoc analysis which can be done, model specific and model agonistic. Direction of post In the next...

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Adding Syntax Highlight

July 19, 2019
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Syntax highlighting Previously, I posted entries without any syntax highlighting as I was satisfied using basic blogdown and Hugo functions until a Disqus member commented in the previous post to use syntax highlighting. Thus, I tasked myself to learn more about syntax highlighting and to implement them in future posts. Now I’d like to share what I’ve learned. There are various...

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Explaining Predictions: Interpretable models (decision tree)

July 2, 2019
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Explaining Predictions: Interpretable models (decision tree)

Introduction This is a follow up post of using simple models to explain machine learning predictions. In the last post, we introduced logistic regression and in today’s entry we will learn about decision tree. We will continue to use the Cleveland heart dataset and use tidymodels principles where possible. The details of the Cleveland heart dataset was also described in the...

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Explaining Predictions: Interpretable models (logistic regression)

June 21, 2019
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Introduction The rise of machine learning In this current 4th industrial revolution, data science has penetrated all industries and healthcare is no exception. There has been an exponential use of machine learning in clinical research in the past decade and it is expected to continue to grow at an even faster rate in the following decade. Many machine learning techniques are...

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Explaining Predictions: Interpretable models (logistic regression)

June 10, 2019
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Introduction The rise of machine learning In this current 4th industrial revolution, data science has penetrated all industries and healthcare is no exception. There has been an exponential use of machine learning in clinical research in the past decade and it is expected to continue to grow at an even faster rate in the following decade. Many machine learning techniques are...

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What’s that disease called? Overview of icd package

May 10, 2019
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What’s that disease called? Overview of icd package

Intro There are many illnesses and diseases known to man. How do the various stakeholders in the medical science industry classify the same illness? The illness will need to be coded in a standardized manner to aid in fair reimbursements and concise reporting of diseases. The International Classification of Diseases (ICD) provides this uniform coding system. The ICD “is the...

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Process Mining (Part 3/3): More analysis and visualizations

May 1, 2019
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Process Mining (Part 3/3): More analysis and visualizations

Intro A week ago, Havard Business Review published an article on process mining and provided reasons for companies to adopt it. If you need a refresher on the concepts of process mining, you can refer to my first post. Conducting process mining is easy with R’s bupaR package. bupaR allows you to create a variety of visualizations as you analyse...

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Process Mining (Part 2/3): More on bupaR package

April 20, 2019
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Recap In the last post, the discipline of event log and process mining were defined. The bupaR package was introduced as a technique to do process mining in R. Objectives for This Post Visualize workflow Understand the concept of activity reoccurrences We will use a pre-loaded dataset sepsis from the bupaR package. This event log is based on real life management of sepsis from...

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