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Great community and great forum. I did enjoy spending some quality time here - I felt it's easier, quicker and more efficient to write up here than on Medium etc directly.

BTW, Jose's post is great - now I know a better way to use Docker on Windows 10, confidently :-) 

I saw this news today. Impressed with Epic's speed to real-world use cases:

https://www.healthcareitnews.com/news/university-minnesota-epic-build-ne...

U of M and Fairview teams will now make the AI tool available for free in the Epic App Orchard.

Drew McCombs, an Epic developer who worked closely with the U of M and Fairview, says customers can install the algorithm via Epic's Cognitive Computing platform and begin end-user training in as few as 10 days.

"Our Cognitive Computing platform quickly pulls the X-ray, runs the algorithm, and shows the resulting prediction directly in Epic software that doctors, nurses, and support staff use every day – speeding up treatment and helping protect staff. The algorithm is available to healthcare organizations around the world that use Epic."

Complementary note for later ref - add in a bit Model Explanatory sample by i.e. SHAP for traditional ML for Random Forrest Classifier at the end of section "Run Training Comparisons of Various Models:"

import shap
# Extract shap values
explainer   = shap.TreeExplainer(models[6][1])  # model[6] is RF, model[7] is XGB
shap_values = explainer.shap_values(X_train_res) 

# Average feature contribution
plt.title('Average Feature Contribution for each Class')
shap.summary_plot(shap_values, X_train_res, plot_type="bar")

# Granular feature contribution plot
plt.title('Feature Contribution According to Value')
shap.summary_plot(shap_values[1], X_train_res, plot_size = (20,10))

Missing Note: Age_Percentile should be included and encoded as well. Experiment function to be included.

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