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height: 200px; position: fixed; bottom: 10px; left: 50px" alt="Sign up to Dash Club to get two free cheat sheets as well as tips and tricks, community apps, and deep dives delivered every two months"> </a> </aside>  <section class="--page-body --tutorial-index --base"> <header class="--welcome"> <div class="--welcome-body">  <div class="--title"> <div class="--body"> <div class="nav-breadcrumb-container"> <div> <div class="breadcrumb-nav"> <a href="/r"> R </a> > <a href="/r/#ai-ml">Artificial Intelligence and Machine Learning</a> > ML Regression </div> </div> <div class="nav-breadcrumb-right"> <div class="--fork"> <a id="forklink" href= "https://github.com/plotly/plotly.r-docs/edit/master/r/2021-07-08-ml-regression.Rmd" > <div class="icon"> <svg style="width:20px;height:20px" viewbox="0 0 24 24"> <path fill="#000000" d="M2.6,10.59L8.38,4.8L10.07,6.5C9.83,7.35 10.22,8.28 11,8.73V14.27C10.4,14.61 10,15.26 10,16A2,2 0 0,0 12,18A2,2 0 0,0 14,16C14,15.26 13.6,14.61 13,14.27V9.41L15.07,11.5C15,11.65 15,11.82 15,12A2,2 0 0,0 17,14A2,2 0 0,0 19,12A2,2 0 0,0 17,10C16.82,10 16.65,10 16.5,10.07L13.93,7.5C14.19,6.57 13.71,5.55 12.78,5.16C12.35,5 11.9,4.96 11.5,5.07L9.8,3.38L10.59,2.6C11.37,1.81 12.63,1.81 13.41,2.6L21.4,10.59C22.19,11.37 22.19,12.63 21.4,13.41L13.41,21.4C12.63,22.19 11.37,22.19 10.59,21.4L2.6,13.41C1.81,12.63 1.81,11.37 2.6,10.59Z"> </path> </svg> </div> Suggest an edit to this page </a> </div> </div> </div> <h1> ML Regression in R </h1> Visualize regression in Tidymodels with Plotly  <details> <summary>New to Plotly?</summary> Plotly is a <a href="/r/is-plotly-free/">free and open-source</a> graphing library for R. We recommend you read our <a href="/r/getting-started/">Getting Started guide</a> for the latest installation or upgrade instructions, then move on to our <a href="/r/plotly-fundamentals/">Plotly Fundamentals tutorials</a> or dive straight in to some <a href="/r/basic-charts/">Basic Charts tutorials</a>. </details> </div> </div> </div> </header>  <section class="tutorial-content"> This page shows how to use Plotly charts for displaying various types of regression models, starting from simple models like <a href="https://parsnip.tidymodels.org/reference/linear_reg.html">Linear Regression</a> and progressively move towards models like Decision Tree and Polynomial Features. We highlight various capabilities of plotly, such as comparative analysis of the same model with different parameters, displaying Latex, and <a href="https://plotly.com/r/3d-surface-plots/">surface plots</a> for 3D data. We will use <a href="https://tidymodels.tidymodels.org/">tidymodels</a> to split and preprocess our data and train various regression models. Tidymodels is a popular Machine Learning (ML) library in R that is compatible with the "tidyverse" concepts, and offers various tools for creating and training ML algorithms, feature engineering, data cleaning, and evaluating and testing models. It is the next-gen version of the popular <a href="http://topepo.github.io/caret/index.html">caret</a> library for R. <h2>Basic linear regression plots</h2> In this section, we show you how to apply a simple regression model for predicting tips a server will receive based on various client attributes (such as sex, time of the week, and whether they are a smoker). We will be using the <a href="https://parsnip.tidymodels.org/reference/linear_reg.html">Linear Regression</a>, which is a simple model that fits an intercept (the mean tip received by a server), and adds a slope for each feature we use, such as the value of the total bill. <h3>Linear Regression with R</h3> <div class="highlight"><pre><code class="language-r" data-lang="r">library(reshape2) # to load tips data library(tidyverse) library(tidymodels) # for the fit() function library(plotly) data(tips) y <- tips$tip X <- tips$total_bill lm_model <- linear_reg() %>% set_engine('lm') %>% set_mode('regression') %>% fit(tip ~ total_bill, data = tips) x_range <- seq(min(X), max(X), length.out = 100) x_range <- matrix(x_range, nrow=100, ncol=1) xdf <- data.frame(x_range) colnames(xdf) <- c('total_bill') ydf <- lm_model %>% predict(xdf) colnames(ydf) <- c('tip') xy <- data.frame(xdf, ydf) fig <- plot_ly(tips, x = ~total_bill, y = ~tip, type = 'scatter', alpha = 0.65, mode = 'markers', name = 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By coloring the training and the testing data points with different colors, you can easily see if the model generalizes well to the test data or not. <div class="highlight"><pre><code class="language-r" data-lang="r">library(reshape2) library(tidyverse) library(tidymodels) library(plotly) data(tips) y <- tips$tip X <- tips$total_bill set.seed(123) tips_split <- initial_split(tips) tips_training <- tips_split %>% training() tips_test <- tips_split %>% testing() lm_model <- linear_reg() %>% set_engine('lm') %>% set_mode('regression') %>% fit(tip ~ total_bill, data = tips_training) x_range <- seq(min(X), max(X), length.out = 100) x_range <- matrix(x_range, nrow=100, ncol=1) xdf <- data.frame(x_range) colnames(xdf) <- c('total_bill') ydf <- lm_model %>% predict(xdf) colnames(ydf) <- c('tip') xy <- data.frame(xdf, ydf) fig <- plot_ly(data = tips_training, x = ~total_bill, y = ~tip, type = 'scatter', name = 'train', mode = 'markers', alpha = 0.65) %>% add_trace(data = tips_test, x = ~total_bill, y = ~tip, type = 'scatter', name = 'test', mode = 'markers', alpha = 0.65 ) %>% add_trace(data = xy, x = ~total_bill, y = ~tip, name = 'prediction', mode = 'lines', alpha = 1) fig </code></pre></div> <div class="plotly html-widget html-fill-item" id="htmlwidget-7efcef6a7ad4d088f646" style="width:672px;height:480px;"></div> <script type="application/json" data-for="htmlwidget-7efcef6a7ad4d088f646">{"x":{"visdat":{"2d9e2daccc9c":["function () ","plotlyVisDat"],"2d9e376ccfa9":["function () ","data"],"2d9e256ebe1f":["function () 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<h2>Comparing different kNN models parameters</h2> In addition to linear regression, it's possible to fit the same data using <a href="http://klausvigo.github.io/kknn/">k-Nearest Neighbors</a>. When you perform a prediction on a new sample, this model either takes the weighted or un-weighted average of the neighbors. In order to see the difference between those two averaging options, we train a kNN model with both of those parameters, and we plot them in the same way as the previous graph. Notice how we can combine scatter points with lines using Plotly. You can learn more about <a href="https://plotly.com/r/graphing-multiple-chart-types/">multiple chart types</a>. <div class="highlight"><pre><code class="language-r" data-lang="r">library(reshape2) library(tidyverse) library(tidymodels) library(plotly) library(kknn) data(tips) y <- tips$tip X <- tips$total_bill # Model #1 knn_dist <- nearest_neighbor(neighbors = 10, weight_func = 'inv') %>% set_engine('kknn') %>% set_mode('regression') %>% fit(tip ~ total_bill, data = tips) # Model #2 knn_uni <- nearest_neighbor(neighbors = 10, weight_func = 'rectangular') %>% set_engine('kknn') %>% set_mode('regression') %>% fit(tip ~ total_bill, data = tips) x_range <- seq(min(X), max(X), length.out = 100) x_range <- matrix(x_range, nrow=100, ncol=1) xdf <- data.frame(x_range) colnames(xdf) <- c('total_bill') y_dist <- knn_dist %>% predict(xdf) y_uni <- knn_uni %>% predict(xdf) colnames(y_dist) <- c('dist') colnames(y_uni) <- c('uni') xy <- data.frame(xdf, y_dist, y_uni) fig <- plot_ly(tips, type = 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Here, we will use <a href="https://parsnip.tidymodels.org/reference/svm_rbf.html"><code>svm_rbf</code></a> with <a href="https://cran.r-project.org/web/packages/kernlab/index.html"><code>kernlab</code></a> engine in <code>regression</code> mode. For generating the 2D mesh on the surface, we use the <a href="https://cran.r-project.org/web/packages/pracma/index.html"><code>pracma</code></a> package. <div class="highlight"><pre><code class="language-r" data-lang="r">library(reshape2) library(tidyverse) library(tidymodels) library(plotly) library(kernlab) library(pracma) #For meshgrid() data(iris) mesh_size <- .02 margin <- 0 X <- iris %>% select(Sepal.Width, Sepal.Length) y <- iris %>% select(Petal.Width) model <- svm_rbf(cost = 1.0) %>% set_engine("kernlab") %>% set_mode("regression") %>% fit(Petal.Width ~ Sepal.Width + Sepal.Length, data = iris) x_min <- min(X$Sepal.Width) - margin x_max <- max(X$Sepal.Width) - margin y_min <- min(X$Sepal.Length) - margin y_max <- max(X$Sepal.Length) - margin xrange <- seq(x_min, x_max, mesh_size) yrange <- seq(y_min, y_max, mesh_size) xy <- meshgrid(x = xrange, y = yrange) xx <- xy$X yy <- xy$Y dim_val <- dim(xx) xx1 <- matrix(xx, length(xx), 1) yy1 <- matrix(yy, length(yy), 1) final <- cbind(xx1, yy1) pred <- model %>% predict(final) pred <- pred$.pred pred <- matrix(pred, dim_val[1], dim_val[2]) fig <- plot_ly(iris, x = ~Sepal.Width, y = ~Sepal.Length, z = ~Petal.Width ) %>% add_markers(size = 5) %>% add_surface(x=xrange, y=yrange, z=pred, alpha = 0.65, type = 'mesh3d', name = 'pred_surface') fig </code></pre></div> <div class="plotly html-widget html-fill-item" id="htmlwidget-dc752af69160d7a3f059" style="width:672px;height:480px;"></div> <script type="application/json" data-for="htmlwidget-dc752af69160d7a3f059">{"x":{"visdat":{"2d9e1763f37e":["function () 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<h2>Prediction Error Plots</h2> When you are working with very high-dimensional data, it is inconvenient to plot every dimension with your output <code>y</code>. Instead, you can use methods such as prediction error plots, which let you visualize how well your model does compared to the ground truth. <h3>Simple actual vs predicted plot</h3> This example shows you the simplest way to compare the predicted output vs. the actual output. A good model will have most of the scatter dots near the diagonal black line. <div class="highlight"><pre><code class="language-r" data-lang="r">library(tidyverse) library(tidymodels) library(plotly) library(ggplot2) data("iris") X <- data.frame(Sepal.Width = c(iris$Sepal.Width), Sepal.Length = c(iris$Sepal.Length)) y <- iris$Petal.Width lm_model <- linear_reg() %>% set_engine('lm') %>% set_mode('regression') %>% fit(Petal.Width ~ Sepal.Width + Sepal.Length, data = iris) y_pred <- lm_model %>% predict(X) db = cbind(iris, y_pred) colnames(db)[4] <- "Ground_truth" colnames(db)[6] <- "prediction" x0 = min(y) y0 = max(y) x1 = max(y) y1 = max(y) p1 <- ggplot(db, aes(x= Ground_truth, y= prediction )) + geom_point(aes(color = "Blue"), show.legend = FALSE) + geom_segment(aes(x = x0, y = x0, xend = y1, yend = y1 ),linetype = 2) p1 <- ggplotly(p1) p1 </code></pre></div> <div class="plotly html-widget html-fill-item" id="htmlwidget-2728a9f93616181ea128" style="width:672px;height:480px;"></div> 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iris_split %>% testing() train_index <- as.integer(rownames(iris_training)) test_index <- as.integer(rownames(iris_test)) iris[train_index,'split'] = 'train' iris[test_index,'split'] = 'test' lm_model <- linear_reg() %>% set_engine('lm') %>% set_mode('regression') %>% fit(Petal.Width ~ Sepal.Width + Sepal.Length, data = iris_training) prediction <- lm_model %>% predict(X) colnames(prediction) <- c('prediction') iris = cbind(iris, prediction) hist_top <- ggplot(iris,aes(x=Petal.Width)) + geom_histogram(data=subset(iris,split == 'train'),fill = "red", alpha = 0.2, bins = 6) + geom_histogram(data=subset(iris,split == 'test'),fill = "blue", alpha = 0.2, bins = 6) + theme(axis.title.y=element_blank(),axis.text.y=element_blank(),axis.ticks.y=element_blank()) hist_top <- ggplotly(p = hist_top) scatter <- ggplot(iris, aes(x = Petal.Width, y = prediction, color = split)) + geom_point() + geom_smooth(formula=y ~ x, method=lm, se=FALSE) scatter <- ggplotly(p = scatter, type = 'scatter') 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<h2>Residual plots</h2> Just like prediction error plots, it's easy to visualize your prediction residuals in just a few lines of codes using <code>ggplotly</code> and <code>tidymodels</code> capabilities. <div class="highlight"><pre><code class="language-r" data-lang="r">library(plotly) library(ggplot2) library(tidyverse) library(tidymodels) data(iris) X <- iris %>% select(Sepal.Width, Sepal.Length) y <- iris %>% select(Petal.Width) set.seed(0) iris_split <- initial_split(iris, prop = 3/4) iris_training <- iris_split %>% training() iris_test <- iris_split %>% testing() train_index <- as.integer(rownames(iris_training)) test_index <- as.integer(rownames(iris_test)) iris[train_index,'split'] = 'train' iris[test_index,'split'] = 'test' lm_model <- linear_reg() %>% set_engine('lm') %>% set_mode('regression') %>% fit(Petal.Width ~ Sepal.Width + Sepal.Length, data = iris_training) prediction <- lm_model %>% predict(X) colnames(prediction) <- c('prediction') iris = cbind(iris, prediction) residual <- prediction - iris$Petal.Width colnames(residual) <- c('residual') iris = cbind(iris, residual) scatter <- ggplot(iris, aes(x = prediction, y = residual, color = split)) + geom_point() + geom_smooth(formula=y ~ x, method=lm, se=FALSE) scatter <- ggplotly(p = scatter, type = 'scatter') violin <- iris %>% plot_ly(x = ~split, y = ~residual, split = ~split, type = 'violin' ) s <- subplot( scatter, violin, nrows = 1, heights = c(1), widths = c(0.65, 0.35), margin = 0.01, shareX = TRUE, shareY = TRUE, titleX = TRUE, titleY = TRUE ) layout(s, showlegend = FALSE) </code></pre></div> <div class="plotly html-widget html-fill-item" id="htmlwidget-70057de72aa62388acea" style="width:672px;height:480px;"></div> <script type="application/json" 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<h3>What About Dash?</h3> <a href="https://dashr.plot.ly/">Dash for R</a> is an open-source framework for building analytical applications, with no Javascript required, and it is tightly integrated with the Plotly graphing library. Learn about how to install Dash for R at https://dashr.plot.ly/installation. Everywhere in this page that you see <code>fig</code>, you can display the same figure in a Dash for R application by passing it to the <code>figure</code> argument of the <a href="https://dashr.plot.ly/dash-core-components/graph"><code>Graph</code> component</a> from the built-in <code>dashCoreComponents</code> package like this: <div class="highlight"><pre><code class="language-r" data-lang="r">library(plotly) fig <- plot_ly() # fig <- fig %>% add_trace( ... ) # fig <- fig %>% layout( ... ) library(dash) library(dashCoreComponents) library(dashHtmlComponents) app <- Dash$new() app$layout( htmlDiv( list( dccGraph(figure=fig) ) ) ) app$run_server(debug=TRUE, dev_tools_hot_reload=FALSE) </code></pre></div> </section>  </section> </div> </main> <script> var array = []; $('.tutorial-content :header:not(h6)').each(function (i, e) { var item = $(e); var itemText = item.text(); var hash = itemText.replace(/[^a-z0-9\s]/gi, '').replace(/[_\s]/g, '-').replace(/^-+|-+$/g, '').toLowerCase(); item.attr("id", hash); array.push({text: itemText, tag: item.prop("tagName"), hash: hash}); }); $("#nav_header_insertion_list").html(array.map(function(entry) { var text = entry.text.replace(/[^a-z0-9\s\.:\-\?\!)]/gi, ''); if(entry.tag.toLowerCase() < "h4"){ return('<li class="--sidebar-item"><a href="#' + entry.hash + '">' + text + '</a></li>'); } else { return('<li class="--sidebar-item"><a href="#' + entry.hash + '">' + text + '</a></li>'); } }).join("")); //$(".tutorial-content :header:not(h6)").append( "\<div class=\"icon copy\" data-tooltip=\"Click to copy direct link\"><svg style=\"width:24px;height:24px\" viewBox=\"0 0 24 24\"><path fill=\"#000000\" d=\"M10.59,13.41C11,13.8 11,14.44 10.59,14.83C10.2,15.22 9.56,15.22 9.17,14.83C7.22,12.88 7.22,9.71 9.17,7.76V7.76L12.71,4.22C14.66,2.27 17.83,2.27 19.78,4.22C21.73,6.17 21.73,9.34 19.78,11.29L18.29,12.78C18.3,11.96 18.17,11.14 17.89,10.36L18.36,9.88C19.54,8.71 19.54,6.81 18.36,5.64C17.19,4.46 15.29,4.46 14.12,5.64L10.59,9.17C9.41,10.34 9.41,12.24 10.59,13.41M13.41,9.17C13.8,8.78 14.44,8.78 14.83,9.17C16.78,11.12 16.78,14.29 14.83,16.24V16.24L11.29,19.78C9.34,21.73 6.17,21.73 4.22,19.78C2.27,17.83 2.27,14.66 4.22,12.71L5.71,11.22C5.7,12.04 5.83,12.86 6.11,13.65L5.64,14.12C4.46,15.29 4.46,17.19 5.64,18.36C6.81,19.54 8.71,19.54 9.88,18.36L13.41,14.83C14.59,13.66 14.59,11.76 13.41,10.59C13,10.2 13,9.56 13.41,9.17Z\"/> </svg> </div>"); </script> <footer class="--footer-main"> <section class="--footer-top"> <div class="--wrap"> <ul class="--footer-body"> <li class="--footer-column"> <h6 style="color:#119dff" class="--footer-heading">JOIN OUR MAILING LIST</h6>			 <ul> 				 <li> Sign up to stay in the loop with all things Plotly — from Dash Club to product updates, webinars, and more! <a href="https://go.plot.ly/subscription" class="subscribe-button" target="_blank"> Subscribe </a> </li> </ul> </li> 		 <li class="--footer-column"> <h6 style="color:#e763fa" class="--footer-heading">Products</h6>			 <ul> <li><a href="https://plotly.com/dash/" target="_self">Dash</a></li> 				 				 <li><a href="https://plotly.com/consulting-and-oem/" target="_self">Consulting and Training</a></li> </ul> </li> 		 <li class="--footer-column"> <h6 style="color:#636efa" class="--footer-heading">Pricing</h6>			 <ul> <li><a href="https://plotly.com/get-pricing/" target="_self">Enterprise Pricing</a></li> 				 </ul> </li> 		 <li class="--footer-column"> <h6 style="color:#00cc96" class="--footer-heading">About Us</h6>			 <ul> <li><a href="https://plotly.com/careers" target="_self">Careers</a></li> 				 <li><a href="https://plotly.com/resources/" target="_self">Resources</a></li> 				 <li><a href="https://medium.com/@plotlygraphs" target="_self">Blog</a></li> 				 </ul> </li> 		 <li class="--footer-column"> <h6 style="color:#EF553B" class="--footer-heading">Support</h6>			 <ul> <li><a href="https://community.plot.ly/" target="_self">Community Support</a></li> 				 <li><a href="https://plotly.com/graphing-libraries" target="_self">Documentation</a></li> 				 </ul> </li> </ul> </div> </section> <section class="--footer-meta"> <div class="--wrap"> <div class="left"> <article class="--copyright">Copyright © 2025 Plotly. 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