### Overview

In this practical you’ll conduct machine learning analyses on a dataset on heart disease. You will see how well many different machine learning models can predict new data. By the end of this practical you will know how to:

1. Create separate training and test data
2. Fit a model to data
3. Explore a model
4. Make predictions from a model
5. Compare models in how well they can predict new data.

### Glossary and packages

Here are the main functions and packages you’ll be using. For more information about the specific models, click on the link in Additional Details.

Algorithm Function Package Additional Details
Regression glm() Base R https://bookdown.org/ndphillips/YaRrr/regression.html#the-linear-model
Fast-and-Frugal decision trees FFTrees() FFTrees https://cran.r-project.org/web/packages/FFTrees/vignettes/guide.html
Random Forests randomForest() randomForest http://www.blopig.com/blog/2017/04/a-very-basic-introduction-to-random-forests-using-r/

### Datasets

You’ll use two datasets in this practical: heartdisease.csv and ACTG175.csv. They available in the data_BaselRBootcamp_Day2.zip file available through the main course page. If you haven’t already, download the data_BaselRBootcamp_Day2.zip folder and unzip it to get the two files.

### Examples

• The following examples will take you through all steps of the machine learning process, from creating training and test data, to fitting models, to making predictions. Follow along and try to see how piece of code works!
# -----------------------------------------------
# A step-by-step tutorial for conducting machine learning
# In this tutorial, we'll see how well 3 different models can
#  predict medical data
# ------------------------------------------------

# -----------------------
# Part A:
# Load libraries
# -----------------------

library(tidyverse)      # for dplyr and ggplot2
library(randomForest)   # for randomForest()
library(FFTrees)        # for FFTrees and the heartdisease data

# -----------------------
# Part B: Create datasets
#  heart_train, heart_test
# -----------------------

heartdisease <- read_csv(file = "data/heartdisease.csv")   # Save a copy of the heartdisease data as heart

set.seed(100)           # To fix the training / test randomization

heartdisease <- heartdisease %>%
mutate_if(is.character, factor) %>%   # Convert character to factor
sample_frac(1)                        # Randomly sort rows

# Savew first 100 rows as heart_train and remaining as heart_test
heart_train <- heartdisease %>%
slice(1:100)

heart_test <- heartdisease %>%
slice(101:nrow(heartdisease))

# ------------------------------
# Part I: Build Models
# ------------------------------

# Build FFTrees_model
FFTrees_model <- FFTrees(formula = sex ~ .,
data = heart_train)

# Build glm_model
glm_model <- glm(formula = factor(sex) ~ .,
data = heart_train,
family = "binomial")  # For predicting a binary variable

# Build randomForest model
randomForest_model <- randomForest(formula = factor(sex) ~ .,
data = heart_train)

# ------------------------------
# Part II: Explore Models
# ------------------------------

print(FFTrees_model)
summary(FFTrees_model)

print(glm_model)
summary(glm_model)

print(randomForest_model)
summary(randomForest_model)

# ------------------------------
# Part III: Training Accuracy
# ------------------------------

# FFTrees training decisions
FFTrees_fit <- predict(object = FFTrees_model,
newdata = heart_train)

# Regression training decisions
#  Positive values are predicted to be 1, negative values are 0
glm_fit <- predict(object = glm_model,
newdata = heart_train) > 0

# randomForest training decisions
randomForest_fit <- predict(object = randomForest_model,
newdata = heart_train)

# Now calculate fitting accuracies and put in dataframe

# Truth value for training data is heart_train$sex train_truth <- heart_train$sex

# Put training results together
training_results <- tibble(model = c("FFTrees", "glm", "randomForest"),
result = c(mean(FFTrees_fit == train_truth),
mean(glm_fit == train_truth),
mean(randomForest_fit == train_truth)))

# Plot training results

ggplot(data = training_results,
aes(x = model, y = result, fill = model)) +
geom_bar(stat = "identity") +
scale_y_continuous(limits = c(0, 1)) +
labs(title = "Training Accuracy",
y = "Correct Classifications")

# ------------------------------
# Part IV: Prediction Accuacy!
# ------------------------------

# Calculate predictions for each model for heart_test

# FFTrees testing decisions
FFTrees_pred <- predict(object = FFTrees_model,
newdata = heart_test)

# Regression testing decisions
#  Positive values are predicted to be 1, negative values are 0
glm_pred <- predict(object = glm_model,
newdata = heart_test) >= 0

# randomForest testing decisions
randomForest_pred <- predict(object = randomForest_model,
newdata = heart_test)

# Now calculate testing accuracies and put in dataframe

# Truth value for test data is heart_test$sex test_truth <- heart_test$sex

testing_results <- tibble(model = c("FFTrees", "glm", "randomForest"),
result = c(mean(FFTrees_pred == test_truth),
mean(glm_pred == test_truth),
mean(randomForest_pred == test_truth)))

# Plot testing results

ggplot(data = testing_results,
aes(x = model, y = result, fill = model)) +
geom_bar(stat = "identity") +
scale_y_continuous(limits = c(0, 1)) +
labs(title = "Testing Accuracy",
y = "Correct Classifications")

## Tasks

• Note, most of this practical will be copying and pasting code from the Examples and only making small changes.
• You should start by copying and pasting all of the code in the examples into a new .R file.
• Try running pieces of the code line by line and understand what it’s doing!

#### Part A: Getting setup

A. Open your BaselRBootcamp project. This project should have the folders R and data in the working directory.

B. Open a new R script and save it in the R folder you just created as a new file called machinelearning_practical.R. At the top of the script, using comments, write your name and the date. The, load the tidyverse, randomForest and FFTrees packages. Here’s how the top of your script should look:

## NAME
## DATE
## Machine Learning Practical

library(tidyverse)      # for dplyr and ggplot2
library(randomForest)   # for randomForest()
library(FFTrees)        # for FFTrees and the heartdisease data

C. Make sure the heartdisease.csv file is in the data in your working directory. Then, using read_csv(), read the heartdisease.csv data and assign it to a new object in R called heartdisease.

D. Explore the heartdisease dataset using summary(), and View()

E. There is a help menu for the heartdisease dataset in the FFTrees package. Look at the help menu for heartdisease by running ?heartdisease

#### Part B: Create training and test data

F. Create separate training dataframes heart_train for model training and heart_test for model testing. Print each of these dataframes to make sure they look ok.

#### Part I: Train models on diagnosis

1. In the example code, we predicted patient’s sex. Now, in our analyses, we will try to predict each patient’s diagnosis using the column diagnosis. To do this, create three new model objects FFTrees_model, glm_model, and randomForest_model, each predicting diagnosis using the training data heart_train

#### Part II: Calculate fits for training data

1. Calculate fits for each model with predict(), then create training_results containing the fitting accuracy of each model in a dataframe. The code will be almost identical to what is in the Example. All you need to do is change the value of truth_train to the correct column in heart_train. Afterwards, plot the results using ggplot. Which model had the best training accuracy?

#### Part III: Explore models

1. Explore each of the three models by applying print(), summary(), plot(), and names() to the objects. Which of these methods work for each object? Can you interpret any of the outputs?

2. You can look at a variable’s importance in each of these models in different ways. In the decision tree, you can look at which variables show up in the tree. In regression, you can look at the significance of the predictors. In random forests, you can look at look at a variable’s importance in terms of what is called mean decrease in gini. Using the following template, explore how each model rates the importance of variables.

# ----------------------
# Explore the importance of different variables in models
# ----------------------

# Print the elements of the FFTrees model
FFTrees_model

# Look at significance of regression
summary(glm_model)

# Look at importance of variables in randomforests
randomForest_model\$importance

#### Part IV: Calculate predictions for test data

1. Calculate predictions for each model based on heart_test, and then calculate the prediction accuracies. Don’t forget to change the value of truth_test to reflect the true value for the current analysis! Then plot the results. Which model predicted each patient’s diagnosis the best?

### Extra Challenges

1. By default, random forests creates 500 trees. You can change this using the ntree argument in randomForest(). Try creating 2 new randomForest objects, one based on either a small number of trees (e.g. 10), and one based on 10,000 trees. How much better (or worse) do these models do compared to the default model with 500 trees?

2. You can use the my.tree argument in FFTrees() to create your own custom tree ‘in words’. To see how this works, look at the “Specifying FFTs directly” vignette in the FFTrees package by running vignette("FFTrees_mytree", package = "FFTrees"). Look through the vignette to see how the my.tree argument works. Then, try creating a new FFTrees object called custom_FFTrees_model trained on the heart_train data using the following rule: “If sex = 0, predict False. If chol > 250, predict True. Otherwise, predict True.”. Plot the object to see how the tree did!

3. A colleague of yours thinks that support vector machines should perform better than the models you used. Is she right? Test her prediction by including support vector machines using the svm() function from the e1071 package in all of your analyses. You’ll need to add code for support vector machines at each stage of the machine learning process, model building, data fitting, and data prediction. Was she right?

4. In all of our machine learning, we have allowed all models to use all data in the heartdisease dataset. However, some of the data is more expensive to collect than other data. What do you think would happen if we only let the models use a few cheap predictors like age, sex and chol? Test your prediction by replicating the machine learning process, but only allow the models to make predictions based on the three variables age, sex and chol. Does one model substantially outperform the others? (Hint: You can easily tell a model what specific variables to include using the formula argument. For example, formula = y ~ a + b will tell a model to model a variable y, but only using variables a and b.)

5. How do you think these algorithms would perform on a randomly generated dataset? Let’s test this by creating a random training and test dataset, and then see how well the algorithms do. Run the code below to add a random column of data called random to heart_train and heart_test. Then, run your machine learning analysis, but now train and test the models to predict the new random data column. How well do the models do in training and testing?

# Add a new column random to heart_train and heart_test

heart_train <- heart_train %>%
mutate(
random = sample(c(0, 1), size = nrow(.), replace = TRUE)
)

heart_test <- heart_test %>%
mutate(
random = sample(c(0, 1), size = nrow(.), replace = TRUE)
)