Macro in the Shell Example Setting-up Gaurd Rails Closing Appendix Related Alternative Other Resources There is many a data science meme degrading excel: (Google Sheets seems to have escaped most of the memes here.) While I no longer use ...

Quantile Regression Example Quantile Regression Forest Review Performance Coverage Interval Width Closing Notes Appendix Residual Plots Other Charts In this post I will build prediction intervals using quantile regression, more specifically, quantile regression forests. This is my third post on prediction intervals. Prior posts: Understanding Prediction Intervals (Part 1) Simulating Prediction ...

Rough Idea Inspiration Procedure Example Simulate Prediction Interval Review Interval Width Coverage Closing Notes Appendix Conformal Inference Other Examples Using Simulation Confusion With Confidence Intervals Adjusting Procedure Alternative Procedure With CV Part 1 of my series of posts on building prediction intervals used data held-out from model training to evaluate the ...

Providing More Than Point Estimates Considering Uncertainty Observation Specific Intervals A Few Things to Know About Prediction Intervals Prediction Intervals and Confidence Intervals Analytic Method of Calculating Prediction Intervals Visual Comparison of Prediction Intervals and Confidence Intervals Inference or Prediction? Cautions With Overfitting Generalizability Review Prediction Intervals Coverage Interval Width ...

Model Performance Metrics Lending Data Example Starter Code Weighting by Classification Outcomes Metrics Across Decision Thresholds Weighting by Observations Closing note Appendix Weights of Observations During and Prior to Modeling Notes on Cost Sensitive Classification Weighted Classification Metrics Questions on Cost Sensitive Classification Arriving at Weights Weighting in predictive modeling ...

Model Performance Metrics Lending Data Example Starter Code Weighting by Classification Outcomes Metrics Across Decision Thresholds Weighting by Observations Closing note Appendix Weights of Observations During and Prior to Modeling Notes on Cost Sensitive Classification Weighted Classification Metrics Questions on Cost Sensitive Classification Arriving at Weights Weighting in predictive modeling ...

Create Data Association of ‘feature’ and ‘target’ Resample Build Models Rescale Predictions to Predicted Probabilities Appendix Density Plots Lift Plot Comparing Scaling Methods TLDR: In classification problems, under and over sampling1 techniques shift the distribution of predicted probabilities towards the minority class. If your problem requires accurate probabilities you will ...

Create Data Association of ‘feature’ and ‘target’ Resample Build Models Rescale Predictions to Predicted Probabilities Appendix Density Plots Lift Plot TLDR: In classification problems, under and over sampling1 techniques shift the distribution of predicted probabilities towards the minority class. If your problem requires accurate probabilities you will need to adjust ...

Load data Feature Engineering & Data Splits Lag Based Features (Before Split, use dplyr or similar) Data Splits Other Features (After Split, use recipes) Model Specification and Training Model Evaluation Appendix Model Building with Hyperparam...

Load data Feature Engineering & Data Splits Lag Based Features (Before Split, use dplyr or similar) Data Splits Other Features (After Split, use recipes) Model Specification and Training Model Evaluation Appendix Model Building with Hyperparam...

Function expecting one column Functions allowing multiple columns Older approaches Appendix dplyr, the foundational tidyverse package, makes a trade-off between being easy to code in interactively at the expense of being more difficult to create...

Function expecting one column Functions allowing multiple columns Older approaches Appendix dplyr, the foundational tidyverse package, makes a trade-off between being easy to code in interactively at the expense of being more difficult to create...

Overview I. Nest and pivot II. Expand combinations III. Filter redundancies IV. Map function(s) V. Return to normal dataframe VI. Bind back to data Functionalize Example creating & evaluating features When is this approach inappropriate? Appen...

Overview I. Nest and pivot II. Expand combinations III. Filter redundancies IV. Map function(s) V. Return to normal dataframe VI. Bind back to data Functionalize Example creating & evaluating features When is this approach inappropriate? Appen...

Riddler express Riddler classic Appendix Time to center Transform grid, rotate first Transform city, pretty This post contains solutions to FiveThirtyEight’s two riddles released 2020-02-14, Riddler Express and Riddler Classic. I created a toy ...

Riddler express Riddler classic Appendix Time to center Transform grid, rotate first Transform city, pretty This post contains solutions to FiveThirtyEight’s two riddles released 2020-02-14, Riddler Express and Riddler Classic. I created a toy ...