title
StatQuest: Random Forests in R

description
Random Forests are an easy to understand and easy to use machine learning technique that is surprisingly powerful. Here I show you, step by step, how to use them in R. NOTE: There is an error at 13:26. I meant to call "as.dist()" instead of "dist()". The code that I used in this video can be found on the StatQuest GitHub: https://github.com/StatQuest/random_forest_demo/blob/master/random_forest_demo.R If you're new to Random Forests, here's a video that covers the basics... https://youtu.be/J4Wdy0Wc_xQ ... and here's a video that covers missing data and sample clustering... https://youtu.be/nyxTdL_4Q-Q For a complete index of all the StatQuest videos, check out: https://statquest.org/video-index/ If you'd like to support StatQuest, please consider... Support StatQuest by buying The StatQuest Illustrated Guide to Machine Learning!!! PDF - https://statquest.gumroad.com/l/wvtmc Paperback - https://www.amazon.com/dp/B09ZCKR4H6 Kindle eBook - https://www.amazon.com/dp/B09ZG79HXC Patreon: https://www.patreon.com/statquest ...or... YouTube Membership: https://www.youtube.com/channel/UCtYLUTtgS3k1Fg4y5tAhLbw/join ...a cool StatQuest t-shirt or sweatshirt: https://shop.spreadshirt.com/statquest-with-josh-starmer/ ...buying one or two of my songs (or go large and get a whole album!) https://joshuastarmer.bandcamp.com/ ...or just donating to StatQuest! https://www.paypal.me/statquest Lastly, if you want to keep up with me as I research and create new StatQuests, follow me on twitter: https://twitter.com/joshuastarmer #statquest #randomforest #ML

detail
{'title': 'StatQuest: Random Forests in R', 'heatmap': [{'end': 301.985, 'start': 262.719, 'weight': 0.738}, {'end': 632.861, 'start': 618.251, 'weight': 0.706}], 'summary': 'Learn to build, use, and evaluate random forests in r using a real dataset from the uci machine learning repository, including data cleaning techniques, model building for heart disease prediction, and analysis of the random forest model with 83.5% out-of-bag error estimate and visualization of error rates for 500 trees.', 'chapters': [{'end': 136.048, 'segs': [{'end': 27.837, 'src': 'embed', 'start': 0.837, 'weight': 0, 'content': [{'end': 8.783, 'text': "You don't need a ukulele to do statistics, but it makes it more fun.", 'start': 0.837, 'duration': 7.946}, {'end': 13.126, 'text': "Hello, I'm Josh Starmer and welcome to StatQuest.", 'start': 10.284, 'duration': 2.842}, {'end': 19.551, 'text': "Today we're going to talk about how to build, use, and evaluate random forests in R.", 'start': 13.706, 'duration': 5.845}, {'end': 25.255, 'text': "This StatQuest builds on two StatQuests that I've already created that demonstrate the theory behind random forests.", 'start': 19.551, 'duration': 5.704}, {'end': 27.837, 'text': "So if you're not familiar with it, check them out.", 'start': 25.775, 'duration': 2.062}], 'summary': "Learn to build, use, and evaluate random forests in r with statquest's josh starmer.", 'duration': 27, 'max_score': 0.837, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/6EXPYzbfLCE/pics/6EXPYzbfLCE837.jpg'}, {'end': 83.884, 'src': 'embed', 'start': 55.396, 'weight': 1, 'content': [{'end': 59.757, 'text': "It overwrites the ggsave function, and that's fine with me, so no worries here.", 'start': 55.396, 'duration': 4.361}, {'end': 64.158, 'text': 'The last library we need to load is random forest.', 'start': 61.218, 'duration': 2.94}, {'end': 73.401, 'text': "Duh, so we can make random forests! It also prints out some stuff in red, but it's no big deal, we can move on from here.", 'start': 65.539, 'duration': 7.862}, {'end': 79.903, 'text': "For this example, we're going to get a real dataset from the UCI machine learning repository.", 'start': 74.761, 'duration': 5.142}, {'end': 83.884, 'text': 'Specifically, we want the heart disease dataset.', 'start': 81.163, 'duration': 2.721}], 'summary': 'Loading necessary libraries, including random forest, for heart disease dataset analysis.', 'duration': 28.488, 'max_score': 55.396, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/6EXPYzbfLCE/pics/6EXPYzbfLCE55396.jpg'}], 'start': 0.837, 'title': 'Building random forests in r', 'summary': 'Introduces building, using, and evaluating random forests in r, using a real dataset from the uci machine learning repository, and explains the process of loading libraries, reading the dataset, and handling data structures.', 'chapters': [{'end': 136.048, 'start': 0.837, 'title': 'Building random forests in r', 'summary': 'Introduces building, using, and evaluating random forests in r, using a real dataset from the uci machine learning repository, and explains the process of loading libraries, reading the dataset, and handling data structures.', 'duration': 135.211, 'highlights': ['The chapter introduces building, using, and evaluating random forests in R It covers the process of building, using, and evaluating random forests in R, which enhances statistical analysis.', 'Using a real dataset from the UCI machine learning repository Demonstrates the practicality of the tutorial by using a real dataset from the UCI machine learning repository, enhancing its application in real-world scenarios.', 'Explains the process of loading libraries, reading the dataset, and handling data structures Provides a step-by-step guide on loading libraries, reading the dataset, and addressing data structure issues, which is crucial for data analysis and modeling.']}], 'duration': 135.211, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/6EXPYzbfLCE/pics/6EXPYzbfLCE837.jpg', 'highlights': ['The chapter introduces building, using, and evaluating random forests in R, enhancing statistical analysis.', 'Demonstrates the practicality of the tutorial by using a real dataset from the UCI machine learning repository.', 'Provides a step-by-step guide on loading libraries, reading the dataset, and addressing data structure issues, crucial for data analysis and modeling.']}, {'end': 468.917, 'segs': [{'end': 222.448, 'src': 'embed', 'start': 169.645, 'weight': 0, 'content': [{'end': 172.948, 'text': "The first thing we do is change the question marks to NA's.", 'start': 169.645, 'duration': 3.303}, {'end': 184.557, 'text': 'Then, just to make the data easier on the eyes, we convert the zeros in sex to F for female and the ones to M for male.', 'start': 174.629, 'duration': 9.928}, {'end': 189.261, 'text': 'Lastly, we convert the column into a factor.', 'start': 186.359, 'duration': 2.902}, {'end': 195.226, 'text': "Then we convert a bunch of other columns into factors, since that's what they're supposed to be.", 'start': 190.962, 'duration': 4.264}, {'end': 201.937, 'text': 'See the UCI website or the sample code on the StatQuest blog for more details.', 'start': 196.914, 'duration': 5.023}, {'end': 211.062, 'text': "Since the CA column originally had a question mark in it, rather than NA, R thinks it's a column of strings.", 'start': 203.938, 'duration': 7.124}, {'end': 215.264, 'text': "We correct that assumption by telling R it's a column of integers.", 'start': 211.682, 'duration': 3.582}, {'end': 218.506, 'text': 'And then we convert it to a factor.', 'start': 216.885, 'duration': 1.621}, {'end': 222.448, 'text': 'Then we do the exact same thing for Thal.', 'start': 219.906, 'duration': 2.542}], 'summary': "Data preprocessing includes converting question marks to na's, sex to f/m, and several columns to factors.", 'duration': 52.803, 'max_score': 169.645, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/6EXPYzbfLCE/pics/6EXPYzbfLCE169645.jpg'}, {'end': 326.03, 'src': 'heatmap', 'start': 262.719, 'weight': 1, 'content': [{'end': 265.16, 'text': "Hooray! We're done with the boring part.", 'start': 262.719, 'duration': 2.441}, {'end': 266.681, 'text': 'Now we can have some fun.', 'start': 265.64, 'duration': 1.041}, {'end': 275.945, 'text': "Since we are going to be randomly sampling things, let's set the seed for the random number generator so that we can reproduce our results.", 'start': 268.601, 'duration': 7.344}, {'end': 282.408, 'text': 'Now we impute values for the NAs in the data set with RF impute.', 'start': 277.746, 'duration': 4.662}, {'end': 289.036, 'text': 'The first argument to RF impute is HD tilde dot.', 'start': 284.073, 'duration': 4.963}, {'end': 297.862, 'text': 'And that means we want the HD, aka heart disease, column to be predicted by the data in all of the other columns.', 'start': 289.717, 'duration': 8.145}, {'end': 301.985, 'text': "Here's where we specify which data set to use.", 'start': 299.543, 'duration': 2.442}, {'end': 305.808, 'text': "In this case, there's only one data set and it's called data.", 'start': 302.665, 'duration': 3.143}, {'end': 313.713, 'text': "Here's where we specify how many random forests RF impute should build to estimate the missing values.", 'start': 307.449, 'duration': 6.264}, {'end': 318.565, 'text': 'In theory, four to six iterations is enough.', 'start': 315.583, 'duration': 2.982}, {'end': 326.03, 'text': "Just for fun, I set this parameter, iter, equal to 20, but it didn't improve the estimates.", 'start': 320.086, 'duration': 5.944}], 'summary': 'Using rf impute to predict heart disease column with 20 iterations, but no improvement in estimates.', 'duration': 78.602, 'max_score': 262.719, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/6EXPYzbfLCE/pics/6EXPYzbfLCE262719.jpg'}, {'end': 382.578, 'src': 'embed', 'start': 355.653, 'weight': 4, 'content': [{'end': 360.434, 'text': "Here's where we actually build a proper random forest using the random forest function.", 'start': 355.653, 'duration': 4.781}, {'end': 371.418, 'text': 'Just like when we imputed values for the NAs, we want to predict HD, aka heart disease, using all of the other columns in the data set.', 'start': 362.335, 'duration': 9.083}, {'end': 377.634, 'text': 'However, this time we specify data.imputed as the dataset.', 'start': 373.151, 'duration': 4.483}, {'end': 382.578, 'text': 'We also want random forest to return the proximity matrix.', 'start': 379.035, 'duration': 3.543}], 'summary': 'Building a random forest model to predict heart disease using all columns in the dataset.', 'duration': 26.925, 'max_score': 355.653, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/6EXPYzbfLCE/pics/6EXPYzbfLCE355653.jpg'}], 'start': 137.225, 'title': 'Data cleaning and model building for heart disease prediction', 'summary': "Discusses data cleaning and conversion techniques such as changing question marks to na's, converting columns into factors, and verifying changes. it also covers rf imputation for missing values and building a random forest model to predict heart disease, specifying the data set, iterations, and model performance assessment.", 'chapters': [{'end': 266.681, 'start': 137.225, 'title': 'Data cleaning and conversion', 'summary': "Discusses the process of cleaning and converting data, including changing question marks to na's, converting sex and heart disease columns into factors, and ensuring appropriate data types, as well as verifying the changes using the stir function.", 'duration': 129.456, 'highlights': ["The chapter emphasizes the need to clean and convert the data to ensure accurate analysis, including changing question marks to NA's, converting sex and heart disease columns into factors, and ensuring appropriate data types (e.g., integers to factors).", "The process involves changing the representation of sex from 0 and 1 to F for female and M for male, converting the zeros in the heart disease column to 'healthy' and the ones to 'unhealthy', and using the STIR function to verify the appropriate changes.", 'The chapter also explains the need to correct the assumption made by R regarding the CA column, originally having a question mark, by converting it to a column of integers and then to a factor, followed by a similar process for the Thal column.']}, {'end': 468.917, 'start': 268.601, 'title': 'Rf imputation and random forest model building', 'summary': 'Covers the process of imputing missing values using rf impute method and building a random forest model to predict heart disease, including specifying the data set, the number of iterations, and assessing the performance of the model.', 'duration': 200.316, 'highlights': ['We specify how many random forests RF impute should build to estimate the missing values, with four to six iterations typically being sufficient. The number of random forests built to estimate missing values is specified, with four to six iterations being considered enough in theory.', 'We impute values for the NAs in the data set with RF impute, setting the seed for the random number generator to reproduce results. Imputation of missing values in the data set using RF impute, while setting the seed for the random number generator to reproduce results.', 'The process of building a random forest model to predict heart disease is demonstrated, including specifying the dataset, and saving the model and associated data. Demonstration of building a random forest model to predict heart disease, involving specifying the dataset, and saving the model and associated data.']}], 'duration': 331.692, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/6EXPYzbfLCE/pics/6EXPYzbfLCE137225.jpg', 'highlights': ["The chapter emphasizes the need to clean and convert the data to ensure accurate analysis, including changing question marks to NA's, converting sex and heart disease columns into factors, and ensuring appropriate data types (e.g., integers to factors).", 'We specify how many random forests RF impute should build to estimate the missing values, with four to six iterations typically being sufficient.', 'We impute values for the NAs in the data set with RF impute, setting the seed for the random number generator to reproduce results.', "The process involves changing the representation of sex from 0 and 1 to F for female and M for male, converting the zeros in the heart disease column to 'healthy' and the ones to 'unhealthy', and using the STIR function to verify the appropriate changes.", 'The process of building a random forest model to predict heart disease is demonstrated, including specifying the dataset, and saving the model and associated data.', 'The chapter also explains the need to correct the assumption made by R regarding the CA column, originally having a question mark, by converting it to a column of integers and then to a factor, followed by a similar process for the Thal column.']}, {'end': 908.702, 'segs': [{'end': 533.469, 'src': 'embed', 'start': 468.917, 'weight': 0, 'content': [{'end': 473.58, 'text': "Since we don't know if 3 is the best value, we'll fiddle with this parameter later on.", 'start': 468.917, 'duration': 4.663}, {'end': 478.674, 'text': "Here's the out-of-bag OOB error estimate.", 'start': 475.452, 'duration': 3.222}, {'end': 485.979, 'text': 'This means that 83.5% of the OOB samples were correctly classified by the random forest.', 'start': 479.235, 'duration': 6.744}, {'end': 490.342, 'text': 'Lastly, we have a confusion matrix.', 'start': 487.74, 'duration': 2.602}, {'end': 496.486, 'text': 'There were 141 healthy patients that were correctly labeled healthy.', 'start': 491.743, 'duration': 4.743}, {'end': 505.072, 'text': 'Hooray! There were 27 unhealthy patients that were incorrectly classified as healthy.', 'start': 497.547, 'duration': 7.525}, {'end': 513.755, 'text': 'Boo! There were 23 healthy patients that were incorrectly classified unhealthy.', 'start': 506.049, 'duration': 7.706}, {'end': 523.982, 'text': 'Boo! Lastly, there were 112 unhealthy patients that were correctly classified unhealthy.', 'start': 514.955, 'duration': 9.027}, {'end': 533.469, 'text': 'Hooray! To see if 500 trees is enough for optimal classification, we can plot the error rates.', 'start': 525.043, 'duration': 8.426}], 'summary': 'Random forest correctly classified 83.5% of oob samples, with 27 unhealthy patients incorrectly labeled as healthy.', 'duration': 64.552, 'max_score': 468.917, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/6EXPYzbfLCE/pics/6EXPYzbfLCE468917.jpg'}, {'end': 650.07, 'src': 'heatmap', 'start': 618.251, 'weight': 0.706, 'content': [{'end': 620.833, 'text': 'And one column for the actual error value.', 'start': 618.251, 'duration': 2.582}, {'end': 624.396, 'text': "And here's the call to ggplot.", 'start': 622.455, 'duration': 1.941}, {'end': 632.861, 'text': 'Bam! The blue line shows the error rate when classifying unhealthy patients.', 'start': 626.197, 'duration': 6.664}, {'end': 638.143, 'text': 'The green line shows the overall out-of-bag error rate.', 'start': 634.782, 'duration': 3.361}, {'end': 643.526, 'text': 'The red line shows the error rate when classifying healthy patients.', 'start': 639.964, 'duration': 3.562}, {'end': 650.07, 'text': 'In general, we see the error rates decrease when our random forest has more trees.', 'start': 645.147, 'duration': 4.923}], 'summary': 'Error rates decrease with more trees in random forest', 'duration': 31.819, 'max_score': 618.251, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/6EXPYzbfLCE/pics/6EXPYzbfLCE618251.jpg'}, {'end': 664.963, 'src': 'embed', 'start': 634.782, 'weight': 2, 'content': [{'end': 638.143, 'text': 'The green line shows the overall out-of-bag error rate.', 'start': 634.782, 'duration': 3.361}, {'end': 643.526, 'text': 'The red line shows the error rate when classifying healthy patients.', 'start': 639.964, 'duration': 3.562}, {'end': 650.07, 'text': 'In general, we see the error rates decrease when our random forest has more trees.', 'start': 645.147, 'duration': 4.923}, {'end': 661.06, 'text': 'If we added more trees, would the error rate go down further? To test this hypothesis, we make a random forest with 1, 000 trees.', 'start': 651.894, 'duration': 9.166}, {'end': 664.963, 'text': 'The out-of-bag error rate is the same as before.', 'start': 662.121, 'duration': 2.842}], 'summary': 'Increasing trees in random forest decreases error rates, but no change with 1000 trees.', 'duration': 30.181, 'max_score': 634.782, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/6EXPYzbfLCE/pics/6EXPYzbfLCE634782.jpg'}, {'end': 799.368, 'src': 'embed', 'start': 774.533, 'weight': 4, 'content': [{'end': 782.782, 'text': 'The third value corresponding to mtri equals 3, which is the default in this case, has the lowest out-of-bag error rate.', 'start': 774.533, 'duration': 8.249}, {'end': 790.09, 'text': "So the default value was optimal, but we wouldn't have known that unless we'd tried other values.", 'start': 784.664, 'duration': 5.426}, {'end': 796.368, 'text': 'Lastly, we want to use the random forest to draw an MDS plot with samples.', 'start': 791.587, 'duration': 4.781}, {'end': 799.368, 'text': 'This will show us how they are related to each other.', 'start': 797.068, 'duration': 2.3}], 'summary': 'The default value for mtri (3) has the lowest out-of-bag error rate in the random forest model. mds plot will illustrate sample relationships.', 'duration': 24.835, 'max_score': 774.533, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/6EXPYzbfLCE/pics/6EXPYzbfLCE774533.jpg'}, {'end': 897.026, 'src': 'embed', 'start': 830.974, 'weight': 5, 'content': [{'end': 833.915, 'text': 'Again, see the other StatQuest for details.', 'start': 830.974, 'duration': 2.941}, {'end': 838.337, 'text': 'Then we format the data for ggplot.', 'start': 836.156, 'duration': 2.181}, {'end': 842.379, 'text': 'And then we draw the graph with ggplot.', 'start': 840.058, 'duration': 2.321}, {'end': 849.451, 'text': 'Triple bam! Unhealthy samples are on the left side.', 'start': 844.079, 'duration': 5.372}, {'end': 853.012, 'text': 'Healthy samples are on the right side.', 'start': 850.971, 'duration': 2.041}, {'end': 859.735, 'text': 'I wonder if patient 253 was misdiagnosed and actually has heart disease.', 'start': 854.813, 'duration': 4.922}, {'end': 866.937, 'text': 'The x-axis accounts for 47% of the variation in the distance matrix.', 'start': 861.595, 'duration': 5.342}, {'end': 873.36, 'text': 'The y-axis only accounts for 14% of the variation in the distance matrix.', 'start': 868.678, 'duration': 4.682}, {'end': 878.895, 'text': 'That means that the big differences are along the x-axis.', 'start': 875.353, 'duration': 3.542}, {'end': 887.441, 'text': "Lastly, if we got a new patient and didn't know if they had heart disease and they clustered down here,", 'start': 880.376, 'duration': 7.065}, {'end': 890.122, 'text': "we'd be pretty confident that they had heart disease.", 'start': 887.441, 'duration': 2.681}, {'end': 897.026, 'text': "Hooray! We've made it to the end of another exciting StatQuest.", 'start': 893.364, 'duration': 3.662}], 'summary': 'Data visualization reveals 47% variation in x-axis, 14% in y-axis. unhealthy samples on left, healthy on right. potential misdiagnosis for patient 253.', 'duration': 66.052, 'max_score': 830.974, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/6EXPYzbfLCE/pics/6EXPYzbfLCE830974.jpg'}], 'start': 468.917, 'title': 'Random forest model analysis', 'summary': 'Evaluates a random forest model with 83.5% out-of-bag error estimate, a confusion matrix indicating correct classification of 141 healthy and 112 unhealthy patients, and visualization of error rates for 500 trees. it explores the relationship between the number of trees and error rates, focusing on classifying healthy and unhealthy patients, ultimately showing that the error rates stabilize after 500 trees. additionally, the chapter discusses optimizing the number of variables in a random forest, finding the optimal value, and creating an mds plot to visualize sample relationships, with the x-axis accounting for 47% and y-axis for 14% of the variation.', 'chapters': [{'end': 533.469, 'start': 468.917, 'title': 'Random forest classification analysis', 'summary': 'Discusses the evaluation of a random forest model with 83.5% out-of-bag error estimate, a confusion matrix depicting correct classification of 141 healthy and 112 unhealthy patients, and a visualization of error rates for 500 trees.', 'duration': 64.552, 'highlights': ['The out-of-bag OOB error estimate indicates that 83.5% of the samples were correctly classified by the random forest model.', 'The confusion matrix shows that 141 healthy and 112 unhealthy patients were correctly classified, with 27 unhealthy patients incorrectly labeled as healthy and 23 healthy patients incorrectly labeled as unhealthy.', 'The analysis suggests plotting error rates to assess if 500 trees provide optimal classification.']}, {'end': 692.234, 'start': 535.216, 'title': 'Random forest error rates analysis', 'summary': 'Explores the creation of a data frame to visualize error rates in a random forest model, demonstrating the relationship between the number of trees and error rates, with a specific focus on classifying healthy and unhealthy patients, ultimately showing that the error rates stabilize after 500 trees.', 'duration': 157.018, 'highlights': ['The error rates decrease as the random forest model has more trees, as demonstrated by the blue line for unhealthy patients, the green line for the overall out-of-bag error rate, and the red line for healthy patients, ultimately stabilizing after 500 trees.', 'Creating a random forest with 1,000 trees did not lead to a decrease in the out-of-bag error rate or an improvement in classifying patients, as indicated by the confusion matrix, highlighting the limited impact of adding more trees on error rates and classification performance.']}, {'end': 908.702, 'start': 694.055, 'title': 'Optimizing random forest and mds plot', 'summary': 'Discusses the process of optimizing the number of variables in a random forest, finding the optimal value, and creating an mds plot to visualize sample relationships, with the x-axis accounting for 47% and y-axis for 14% of the variation.', 'duration': 214.647, 'highlights': ['We experiment with different numbers of variables at each step, testing values between 1 and 10, and find that the default value of 3 for mtri in the random forest has the lowest out-of-bag error rate, demonstrating the optimal choice.', 'We utilize the random forest to generate an MDS plot, where unhealthy samples are on the left, healthy samples on the right, and the x-axis accounts for 47% of the variation in the distance matrix, providing valuable insight into sample relationships.', 'We employ CMD scale to calculate the percentage of variation in the distance matrix that the X and Y axes account for, with the y-axis only accounting for 14% of the variation, emphasizing the importance of the x-axis in capturing the big differences.']}], 'duration': 439.785, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/6EXPYzbfLCE/pics/6EXPYzbfLCE468917.jpg', 'highlights': ['The out-of-bag OOB error estimate indicates that 83.5% of the samples were correctly classified by the random forest model.', 'The confusion matrix shows that 141 healthy and 112 unhealthy patients were correctly classified, with 27 unhealthy patients incorrectly labeled as healthy and 23 healthy patients incorrectly labeled as unhealthy.', 'The error rates decrease as the random forest model has more trees, as demonstrated by the blue line for unhealthy patients, the green line for the overall out-of-bag error rate, and the red line for healthy patients, ultimately stabilizing after 500 trees.', 'Creating a random forest with 1,000 trees did not lead to a decrease in the out-of-bag error rate or an improvement in classifying patients, as indicated by the confusion matrix, highlighting the limited impact of adding more trees on error rates and classification performance.', 'The default value of 3 for mtri in the random forest has the lowest out-of-bag error rate, demonstrating the optimal choice.', 'We utilize the random forest to generate an MDS plot, where unhealthy samples are on the left, healthy samples on the right, and the x-axis accounts for 47% of the variation in the distance matrix, providing valuable insight into sample relationships.', 'The y-axis only accounts for 14% of the variation, emphasizing the importance of the x-axis in capturing the big differences.']}], 'highlights': ['The out-of-bag OOB error estimate indicates that 83.5% of the samples were correctly classified by the random forest model.', 'The confusion matrix shows that 141 healthy and 112 unhealthy patients were correctly classified, with 27 unhealthy patients incorrectly labeled as healthy and 23 healthy patients incorrectly labeled as unhealthy.', 'The error rates decrease as the random forest model has more trees, as demonstrated by the blue line for unhealthy patients, the green line for the overall out-of-bag error rate, and the red line for healthy patients, ultimately stabilizing after 500 trees.', 'Creating a random forest with 1,000 trees did not lead to a decrease in the out-of-bag error rate or an improvement in classifying patients, as indicated by the confusion matrix, highlighting the limited impact of adding more trees on error rates and classification performance.', 'The default value of 3 for mtri in the random forest has the lowest out-of-bag error rate, demonstrating the optimal choice.', 'We utilize the random forest to generate an MDS plot, where unhealthy samples are on the left, healthy samples on the right, and the x-axis accounts for 47% of the variation in the distance matrix, providing valuable insight into sample relationships.', 'The y-axis only accounts for 14% of the variation, emphasizing the importance of the x-axis in capturing the big differences.', 'The chapter introduces building, using, and evaluating random forests in R, enhancing statistical analysis.', 'Demonstrates the practicality of the tutorial by using a real dataset from the UCI machine learning repository.', 'Provides a step-by-step guide on loading libraries, reading the dataset, and addressing data structure issues, crucial for data analysis and modeling.', "The chapter emphasizes the need to clean and convert the data to ensure accurate analysis, including changing question marks to NA's, converting sex and heart disease columns into factors, and ensuring appropriate data types (e.g., integers to factors).", 'We specify how many random forests RF impute should build to estimate the missing values, with four to six iterations typically being sufficient.', 'We impute values for the NAs in the data set with RF impute, setting the seed for the random number generator to reproduce results.', "The process involves changing the representation of sex from 0 and 1 to F for female and M for male, converting the zeros in the heart disease column to 'healthy' and the ones to 'unhealthy', and using the STIR function to verify the appropriate changes.", 'The process of building a random forest model to predict heart disease is demonstrated, including specifying the dataset, and saving the model and associated data.', 'The chapter also explains the need to correct the assumption made by R regarding the CA column, originally having a question mark, by converting it to a column of integers and then to a factor, followed by a similar process for the Thal column.']}