FFTrees
An R package to create, visualise, and impliment fast and frugal decision trees
Nathaniel Phillips, Economic Psychology, University of Basel
BaselR Meeting - March 2017
A growing problem at the Cook County Hospital in 1996
Patient overload
Diagnosing ER patients with chest pain.
Situation
- 30 people a day worried about a heart attack.
- Coronary care bed costs $2,000 a night and requires a 3 day stay.
Decision task
- Send people with heart attacks to the coronary care bed, and healthy patients to a normal bed.
Multiple, uncertain measures
- Blood pressure, Stethescope,
- Questions: How long? How much? During exercise? History? Cholesterol? Drugs? etc.
- Electrocardiogram (ECG) reading.
Intuition based on clinical expertise was not working.
Study: How much do doctors agree on diagnoses?
- Asked doctors to estimate from 0 to 100 the probability of a heart attack of 20 separate patients.
Conclusion: Terribly low agreement
"In each case the answers we got pretty much ranged from 0 to 100. It was extraordinary" (Brenden Reilly, Department of Medicine chairman)
Solution
- A decision tree developed by a cardiologist named Lee Goldman.
Why use a decision tree?
- Speed, Easy of understanding and implementation
The Cook hospital decision tree
- Over two years, the performance of the tree was compared to the physician's intuitive judgments.
Results
Tree dramatically outperformed the doctor's clinical judgments and resulted in far fewer false-positives and huge cost savings
To this day, the tree is still used at the hospital.
Fast and frugal decision trees (FFT)
A fast and frugal decision tree (FFT) is a very simple, highly restricted decision tree.
In an FFT, each node has exactly two branches, where at least one branch is an exit branch (Martignon et al., 2008).
Because of their restrictions, FFTs are even faster and require less information than non-FFT trees.
Depression Tree
- Jenny et al. (2013): Simple rules for detecting depression
Bank failure
- Neth et al. (2013): Homo heuristics in the financial world: From risk management to managing uncertainty
Problem
There is no off-the-shelf method to construct FFTs
Task
- Create an easy-to-use R package that constructs, visualizes, and implements FFTs called
FFTrees.
FFTrees
# Available on CRAN
install.packages("FFTrees")
devtools::github("ndphillips/FFTrees", include_vignette = TRUE)
Heart disease datatset
library(FFTrees)
head(heartdisease)
## age sex cp trestbps chol fbs restecg thalach exang oldpeak slope ca
## 1 63 1 ta 145 233 1 hypertrophy 150 0 2.3 down 0
## 2 67 1 a 160 286 0 hypertrophy 108 1 1.5 flat 3
## 3 67 1 a 120 229 0 hypertrophy 129 1 2.6 flat 2
## 4 37 1 np 130 250 0 normal 187 0 3.5 down 0
## 5 41 0 aa 130 204 0 hypertrophy 172 0 1.4 up 0
## 6 56 1 aa 120 236 0 normal 178 0 0.8 up 0
## thal diagnosis
## 1 fd 0
## 2 normal 1
## 3 rd 1
## 4 normal 0
## 5 normal 0
## 6 normal 0
Creating a Heart Disease FFT
# Step 1: Create training and test data
set.seed(100)
heartdisease <- heartdisease[sample(nrow(heartdisease)),]
heart.train <- heartdisease[1:150,]
heart.test <- heartdisease[151:303,]
# Step 2: Create heart.fft
heart.fft <- FFTrees(formula = diagnosis ~.,
data = heart.train,
data.test = heart.test)
Evaluating a decision algorithm
FFT summary statistics
# Step 3: Summary statistics
heart.fft
## [1] "7 FFTs using up to 4 of 13 cues"
## [1] "FFT #4 uses 3 cues {thal,cp,ca} with the following performance:"
## train test
## n 150.00 153.00
## pci 0.88 0.88
## mcu 1.74 1.73
## acc 0.80 0.82
## bacc 0.80 0.82
## sens 0.82 0.88
## spec 0.79 0.76
Heart disease cue accuracies
plot(heart.fft, what = "cues", main = "Heart Disease")
Heart Disease FFT
plot(heart.fft, main = "Heart Disease",
decision.names = c("healthy", "sick"),
stats = FALSE)
Heart Disease FFT - Training
Heart Disease FFT - Prediction
Heart Disease FFT - Tree 3
Heart Disease FFT - Tree 6
How do FFTs compare to regression and CART?
- Simplicity: How much information is used and how is it combined?
- Accuracy: How well can the algorithm predict new data?
Heart disease: regression
- 4 significant cues: (sex, cp, trestbps, ca)
Heart disease: rpart
- 8 cues (thal, cp, oldpeak, ca, age, exang, thalach, chol)
Heart disease: FFT
- 3 cues (thal, cp, ca)
- However, when applied to the data, only about 1.75 cues are even used on average. As a result, > 85% of the cue information is completely ignored.
Heart disease classification accuracy
Heart disease classification accuracy
Heart disease classification accuracy
How accurate are FFTs built by FFTrees?
- Prediction competition
- 10 datasets taken from the UCI machine learning database
- 50% Fitting / 50% Prediction subsample splitting, DV: balanced accuracy = (sensitivity + specificity) / 2
| dataset | cases | cues | base.rate |
|---|---|---|---|
| arrhythmia | 68 | 280 | 0.29 |
| audiology | 226 | 70 | 0.10 |
| breast | 683 | 10 | 0.35 |
| bridges | 92 | 10 | 0.39 |
| cmc | 1473 | 10 | 0.35 |
Table: 5 of the 10 prediction datasets
Aggregate simulation prediction results
Aggregate simulation prediction results
Aggregate simulation prediction results
Simulation prediction results by dataset
Conclusions
- FFTrees makes it easy to develop simple, effective, transparent decision trees.
- Decision aids built with FFTrees can compete with complex, compensatory algorithms in prediction.
Next steps
- Speed up code with c++ or Julia.
- Include cue costs into algorithm.
- Heart disease FFT: $75.91
- Regression: $300
- Quantify when and how a tree fails when it is applied to data over time.
Questions?
Tree construction algorithm
| Step | Time |
|---|---|
| 1. Decision threshold for each cue | |
| 2. Select cues | 2 |
| 3. Order cues | 4 |
| 4. Exit branch | 6 |
Exploring trees with FFForest()
The
FFForest()function will create a forest of FFTs by creating trees from random subsets of the data.You can then extrapolate cue importance and co-occurence from an
FFForestobject: