Posted in Machine Learning, Stata
Rahul Raoniar- posted on
- Comments Off on Survival Analysis in Stata (KM Estimate, Cox-PH and AFT Model)
Learn Survival Analysis (Time to Event Analysis) in Stata for Research
Posted in Machine Learning
- Rahul Raoniar
- posted on
- Comments Off on Survival Analysis in Python (KM Estimate, Cox-PH and AFT Model)
Learn Survival Analysis (Time to Event Analysis) in Python for Research
Posted in Machine Learning, R
- Rahul Raoniar
- posted on
- Comments Off on Modelling Binary Logistic Regression using Tidymodels Library in R (Part-1)
Step by step guide to fit logistic regression using tidymodels library
Posted in Machine Learning
- Rahul Raoniar
- posted on
- Comments Off on Model Hyperparameters Tuning using Grid, Random and Genetic based Search in Python
Machine Learning Model Tuning using Grid, Random and Genetic Algorithm based Search.
Posted in Machine Learning
- Rahul Raoniar
- posted on
- Comments Off on Fitting MLR and Binary Logistic Regression using Python
Learn how to estimate regression models in Python for research publications.
Posted in Machine Learning
- Rahul Raoniar
- posted on
- Comments Off on Machine Learning Model Explanation using Shapley Values
Learn how to interpret a BlackBox model using SHAP (SHapley Additive exPlanations)
Posted in Machine Learning, Python
- Rahul Raoniar
- posted on
- Comments Off on Diabetes Prediction Model Explanation using LIME
Train, test and explain a Diabetes classification model in Python using LIME
Posted in Machine Learning, R
- Rahul Raoniar
- posted on
- Comments Off on Modelling Multiple Linear Regression Using R
Learn how to train an MLR model, interpret its coefficients and perform a step-wise regression.
Posted in Machine Learning, R
- Rahul Raoniar
- posted on
- Comments Off on Decision Tree-Based Diabetes Classification in R
Decision Tree Training, Pruning and Hyperparameters Tuning.
Posted in Machine Learning, Python
- Rahul Raoniar
- posted on
- Comments Off on Diabetes Classification using PyCaret
Model comparison, hyper-parameter tuning and plotting in a few lines of codes
