1. Recap 2 Tune again Modelling Retuning 2.1 Retune Random Forest 2.2 Retune Prophet boost 2.3 Performance (after retuning) 3 Ensemble 3.1 Peformance (ensemble) 4 Performance (individual levels) Hospital Cluster level National level 5 The future Hospital Cluster National 6 KIV Plans Errors 1. Recap The aim of this series of blog is to predict monthly admissions to Singapore public acute ...

1 Intro 2 Cross validation Metrics 3. Pre-processing 3.1 Base recipe 3.2 Spline recipe 3.3 Prophet boost recipe 4 Modelling 4.1 GLM 4.2 MARS 4.3 RF 4.4 XGB 4.5 Prophet boost 5. Evaluation Conclusion 1 Intro The aim of this series of blog...

1 Intro 2 Data wrangling 2.1 Long format with aggregated values 2.2 Extend into the future 2.3 External regressor 2.3.1 Lags and rolling lags 2.3.2 Covid 2.3.3 Time series features 3 Splitting 4 Pre-processing recipes Pre-processing order 5....

Intro Reconciliation Dataset Feature Engineering Models Evaluation ARIMA ARIMA vs ETS Performance for each level Cluster level National level Conclusion Error Intro The aim of this series of blog is do predict monthly admissions to Singapore public acute adult hospitals. The dataset starts from Jan 2016 and ends in Feb 2021. EDA ...

Introduction The aim of this series of blog is to predict monthly admissions to Singapore public acute adult hospitals. The dataset starts from Jan 2016 and ends in Feb 2021. library(tidyverse) library(timetk) library(fpp3) # cleaned up dataset d...

Introduction Visualization 1. Trend 2. Seasonality Trend and seasonality 3. Anomaly Conclusion Introduction The aim of this series of blogs is to do time series forecasting with libraries that conform to tidyverse principles and there are two of these time series meta-packages modeltime which is created to be the time series equivalent of ...

This post is a supplementary material for an assignment. The assignment is part of the Augmented Machine Learning unit for a Specialised Diploma in Data Science for Business. The aim of this project is to classify if patients with Community Acquired Pneumonia (CAP) became better after seeing a doctor or ...

This post is a supplementary material for an assignment. The assignment is part of the Augmented Machine Learning unit for a Specialised Diploma in Data Science for Business. The aim of this project is to classify if patients with Community Acquired Pneumonia (CAP) became better after seeing a doctor or ...

Intro This post is a supplementary material for an assignment. The assignment is part of the Augmented Machine Learning unit for a Specialised Diploma in Data Science for Business. The aim of the assignment is to use DataRobot for predictive modelling. Exploratory data analysis and feature engineering will be done ...

Intro 9 Lab_ related category Missing Lab values Outlier Lab values High Lab_Hb levels Low Lab_Neu High Lab_Sugar 10 CS_ cultures related category 11 Abx_ antibiotics related category 11i Class of empirical antibiotics given 11ii Antibiotics given Number of antibiotics given 11iii Duration of antibiotics 12 Care_ continuum of care status ...

Intro Data dictionary EDA blueprint Outcome Discard the noise 1 Other_ related category 2 Pt_ Patient related category Appropriate patients Case_number Age 3 R_ Radiology related category Effusion and effusion site On chest x-ray ( R_CXR_effusion, R_CXR_effusionSite) On CT chest (R_CT_effusion, R_CT_effusionSite) 4 SS_ Category related ...

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 ...

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 ...