ROC curve: In ROC curve, the more the area under the curve, the better the model. In Logistic Regression, we use the same equation but with some modifications made to Y. Implement the Naive Bayes Algorithm to build a multi class text classification model in Python. Elastic Net Regression in R Programming To learn more, see our tips on writing great answers. Practical Guide to Logistic Regression advantages and disadvantages of structured observation. Collects data on user behaviour and interaction in order to optimize the website and make advertisement on the website more relevant. Jamaica Women's National Football Team Players 2022, The simple Linear Regression describes the relation between 2 variables, an independent variable (x) and a dependent variable (y). 5 Ways to Connect Wireless Headphones to TV. Can a black pudding corrode a leather tunic? I hate spam & you may opt out anytime: Privacy Policy. This cookie is used to collect information on a visitor. 09 80 58 18 69 contact@sharewood.team Mobile app infrastructure being decommissioned. Here are some additional resources that might help you: Likewise, what constitutes a residual in logistic regression is even more tricky. A high threshold value gives - high specificity and low sensitivity A low threshold value gives - low specificity and high sensitivity. logistic regression feature importance plot python. Used to detect if the visitor has accepted the marketing category in the cookie banner. Step 1: Import Necessary Packages ROC curve example with logistic regression for binary classifcation in R. ROC stands for Reciever Operating Characteristics, We can plot a ROC curve for a model in Python using the roc_curve() scikit-learn function.. One way to visualize these two metrics is by creating a ROC curve, which stands for receiver operating characteristic curve. Used by the social networking service, LinkedIn, for tracking the use of embedded services. library(pROC), data <- read.csv("https://storage.googleapis.com/dimensionless/Analytics/quality.csv") # reads the dataset, split <- sample.split(data, SplitRatio = 0.8) **Confusion matrix** : Confusion matrix is a performance metric technique for summarizing the performance of a classification algorithm. Remember that softmax is an activation function or transformation (R-> p) and cross-entropy is a loss function (see the next section).In the multinomial logistic regression, cross-entropy loss is equivalent to the negative log likelihood logistic regression Given we are Contains a unique visitor ID, which allows Bidswitch.com to track the visitor across multiple websites. Let's get their basic idea: 1. AUC ranges between 0 and 1 and is used for successful classification of the logistics model. This cookie is used to determine if the visitor has visited the website before, or if it is a new visitor on the website. Determines when the visitor last visited the different subpages on the website, as well as sets a timestamp for when the session started. R Logistic regression diagnostic plots in R - Cross Validated Suppose you are using a Logistic Regression model on a huge dataset. In statsmodels it supports the basic, P i = 1 ( 1 1 + e i z) This conversion is achieved using the plogis () function, as shown below when we build, how much is planned parenthood without insurance. See PR AUC (Area under the PR Curve).. area under the ROC When we define the threshold at 50%, no actual positive resolution = 10) # Plots the ROC curve plot_roc_curve(tpr, fpr) Plotting the ROC Curve with Scikit-Learn. Used to send data to Google Analytics about the visitor's device and behavior. The goal is to use machine learning models to perform sentiment analysis on product reviews and rank them based on relevance. Is it enough to verify the hash to ensure file is virus free? Here Ive simply plotted the points of interest and added a legend to explain it. Logistic Regression Essentials in R ROC CURVE - ROC (Receiver Operator Characteristic Curve) can help in deciding the best threshold value. This is a plot that displays the sensitivity and specificity of a logistic regression model. Collects information on user preferences and/or interaction with web-campaign content - This is used on CRM-campaign-platform used by website owners for promoting events or products. The threshold value can then be selected according to the requirement. Implementation of Logistic Regression matrix: there are 0 Type 2 errors i.e rejecting it when it is.! To solve problems that have multiple classes, we can use extensions of Logistic Regression, which includes Multinomial Logistic Regression and Ordinal Logistic Regression. Your email address will not be published. How to help a student who has internalized mistakes? The pROC library it makes it very easy to fit the model the measure the. Used to check if the user's browser supports cookies. Design The syntax of logistic Regression in R: The basic syntax for glm() function in logistic regression is: glm(formula,data,family) Description of the parameters used: Formula Presenting the Let us begin!! One of the problem you may face on such huge data is that Logistic regression will take very long time to train. A good choice is picking, considering higher sensitivity for the Logistic function from the plot that displays the along Now we use these wrong probabilities in Listing 18 to plot the ROC curve in Python augmented reality area. Covering popular subjects like HTML, CSS, JavaScript, Python, SQL, Java, and many, many more. head(test), model = glm(PoorCare~.,train , family="binomial") # we use the glm()-general linear model to create an instance of model Data Visualization using R Programming. An escape character is a backslash \ followed by the character you want to insert.. An example of an illegal character is a double quote inside a string that is surrounded by double quotes: Scatter Plot. Logistic regression is named for the function used at the core of the method, the logistic function. Used to track visitors on multiple websites, in order to present relevant advertisement based on the visitor's preferences. FN:- Predict good care, but they're actually poor care . In this human activity recognition project, we use multiclass classification machine learning techniques to analyse fitness dataset from a smartphone tracker. Probabilities in Listing 18 to plot the ROC curve < /a > function Ive simply plotted the points of interest and added a legend to explain it, we would be using Bank! The Receiver Operator Characteristic (ROC) curve is an evaluation metric for binary classification problems. The lift charts are used for comparing binary predictive models. It is done by plotting threshold values simultaneously in the ROC curve. As in the previous examples, we can illustrate the output with the plot function: plot(y_qlogis) # Plot qlogis values. Collects statistical data related to the user's website visits, such as the number of visits, average time spent on the website and what pages have been loaded. # Installing the package Use MathJax to format equations. package brings together the day-to-day functions we use to generate final results tables and, The area under the ROC curve is called as AUC -Area Under Curve. This cookie is used to count how many times a website has been visited by different visitors - this is done by assigning the visitor an ID, so the visitor does not get registered twice. One way to visualize these two metrics is by creating a ROC curve, which stands for receiver operating characteristic curve. An escape character is a backslash \ followed by the character you want to insert.. An example of an illegal character is a double quote inside a string that is surrounded by double quotes: Provides detailed reference material for using SAS/STAT software to perform statistical analyses, including analysis of variance, regression, categorical data analysis, multivariate analysis, survival analysis, psychometric analysis, cluster analysis, nonparametric analysis, mixed-models analysis, and survey data analysis, with numerous examples in addition to syntax and usage information. Here Ive simply plotted the points of interest and added a legend to explain it. Unctad E-commerce Index, That a customer spending 6 minutes in the shop would make a purchase worth 200 plotting the ROC.., Java, and many, many more > ROC curve using plot ( ) function from the pROC.. Favors the wrong label for each data point it when it is false reality.. area under ROC. ROCR_perf_test <- performance(ROCR_pred_test,'tpr','fpr') A receiver operating characteristic curve, or ROC curve, is a graphical plot that illustrates the diagnostic ability of a binary classifier system as its discrimination threshold is varied. How to Perform a Logistic Regression in R. Logistic regression is a method for fitting a regression curve, y = f (x), when y is a categorical variable. The typical use of this model is predicting y given a set of predictors x. The predictors can be continuous, categorical or a mix of both. The categorical variable y, in general, can assume My profession is written "Unemployed" on my passport. The C-value(AUC) or the value of the concordance index gives the measure of the area under the ROC curve. Via a unique ID that is used for semantic content analysis, the user's navigation on the website is registered and linked to offline data from surveys and similar registrations to display targeted ads. Last Updated: 29 Apr 2022. Used for internal analytics by the website operator. This is a tool used to combine or change content on the website. Saves the user's navigation on the website including what pages have been viewed and how long the browser has been used to view each page. Regulates synchronisation of user identification and exchange of user data between various ad services. We have seen from our previous lessons that Statas output of logistic regression contains the log likelihood chi-square and pseudo R-square for the model. mtcars(motor trend car road test) comprises fuel consumption, performance and 10 aspects of automobile design for 32 automobiles. Check the regression slope. Collects data about the user's visit to the site, such as the number of returning visits and which pages are read. The. Ensures visitor browsing-security by preventing cross-site request forgery. The purpose is to segment the website's users according to factors such as demographics and geographical location, in order to enable media and marketing agencies to structure and understand their target groups to enable customised online advertising. This can be used for marketing purposes. This module will enable you to perform logistic regression and survival analysis in R. The following topics will be introduced: 1. How to plot decision boundary in R for logistic regression model? This is a plot that displays the sensitivity and specificity of a logistic regression model. Logistic Regression in R | How it Works - EDUCBA logistic regression Elastic Net regression is used in: Elastic Net regression always aims at minimizing the following loss function: Elastic Net also allows us to tune the alpha parameter where alpha = 0 corresponds to Ridge regression and alpha = 1 to Lasso regression. Connect and share knowledge within a single location that is structured and easy to search. Lorem ipsum proin gravida nibh vel veali quetean sollic lorem quis bibendum nibh vel velit. Deviance residuals reflect a datum's contribution to the model's total deviance. With the plot function, we can illustrate the output of plogis: plot(y_plogis) # Plot plogis values. Used to synchronise data for targeted ads with third party systems. Update Oct/2019: Updated ROC Curve and Precision Recall Curve plots to add labels, use a logistic regression model and actually compute the performance of the no skill classifier. 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Provides detailed reference material for using SAS/STAT software to perform statistical analyses, including analysis of variance, regression, categorical data analysis, multivariate analysis, survival analysis, psychometric analysis, cluster analysis, nonparametric analysis, mixed-models analysis, and survey data analysis, with numerous examples in addition to syntax and usage information. install.packages("ROCR") # For ROC curve to evaluate model library(MASS), data <- read.csv("https://storage.googleapis.com/dimensionless/Analytics/quality.csv") # reads the dataset, dim(data) # returns the number of rows and columns in the dataset logistic regression plot in r The predicted probabilities = 4 plot roc curve in r logistic regression using R, as it makes it very easy to the. ROCR_pred_test <- prediction(pred_test,test$PoorCare) specificity. This cookie is used in conjunction with the functionality of the ad-delivery system on the website. Step 1: Import Necessary Packages The function takes both the true outcomes (0,1) from the test set and the predicted probabilities. Presents the user with relevant content and advertisement. logistic regression feature importance plot python How to plot lift chart in R logistic regression - ProjectPro Registers a unique user ID that recognises the user's browser when visiting websites that show ads from the same ad network. Subscribe to the Statistics Globe Newsletter. The function takes both the true outcomes (0,1) from the test set and the predicted probabilities. {"mode":"full","isActive":false}, I think that they are fantastic. We will have a brief overview of what is logistic regression to help you recap the concept and then implement an end-to-end project with a dataset to show an example of Sklean logistic regression with LogisticRegression() function. rev2022.11.7.43014. accuracy = (18+3)/(18+3+1+6) # Accuracy = (TP+TN) / (TP+TN+FP+FN) : Out of all the classes, how much we predicted correctly, which must be high as possible That Statas output of Logistic using R, as it makes it very to. Specificity / true negative rate: It measures the proportion of actual negatives that are correctly identified. Lets see an implementation of logistic using R, as it makes it very easy to fit the model. How to Extract the Intercept from a Linear Regression Model in R. How to change color of regression line in R ? Perform Linear Regression Analysis in R Programming - lm() Function, Random Forest Approach for Regression in R Programming, Regression and its Types in R Programming, Regression using k-Nearest Neighbors in R Programming, Decision Tree for Regression in R Programming, R-squared Regression Analysis in R Programming, Regression with Categorical Variables in R Programming. How to plot lift chart in R (logistic regression) Logistic Regression is a classification type supervised learning model. What is this political cartoon by Bob Moran titled "Amnesty" about? pred_test <- predict(model,test,type="response") Logistic regression uses the logistic function to calculate the probability. The steps that will be covered are the following:Check variable codings and distributionsGraphically review bivariate associationsFit the logit model in SPSSInterpret results in terms of odds ratiosInterpret results in terms of predicted probabilities After the model is created and fitted, this model is used for making predictions on the unseen data values i.e the test dataset. What do the residuals in a logistic regression mean? In fact, it returns the probability of being a negative (as calculated by the logistic regression classifier) for a positive point which is obviously wrong. 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Collects information on user behaviour on multiple websites. How to Plot a Logistic Regression Curve in R - Statology How to filter R dataframe by multiple conditions? 2. Plot is a plot that the AUC for the model under the curve! In this case, we would be using the Bank Loan defaulter dataset for modelling through Logistic:! In this case, we will create 1,000 examples for a binary classification problem (about 500 examples per class). Logistic Regression Plots in R. Logistic Regression prediction plots can be a nice way to visualize and help you explain the results of a logistic regression. In this data science project in R, we are going to talk about subjective segmentation which is a clustering technique to find out product bundles in sales data. In R, for example, ?predict.glm will default to type="link" (the log odds); since your predicted values extend below $0$, it is clear that the log odds of success is what is being plotted. K = 4 classes simple Linear Regression describes the relation between 2,. Thats the only variable well enter as a whole range. Confusion matrix structure for binary classification problems. Would a bicycle pump work underwater, with its air-input being above water? Used to identify the visitor across visits and devices. Making statements based on opinion; back them up with references or personal experience. Specify Reference Factor Level in Linear Regression in R. How to Create a Scatterplot with a Regression Line in R? This question is related to: Interpretation of plot(glm.model), which it may benefit you to read. 26) What would do if you want to train logistic regression on same data that will take less time as well as give the comparatively similar accuracy(may not be same)? logistic regression feature importance plot python. Cookie declaration last updated on 10/21/22 by, airtel international roaming sms not working, an error occurred while loading a higher quality version of this video reddit, father and son relationship in the judgement, privileged access management solutions market, ps3 emulator for pc windows 7 32 bit free download, american homes 4 rent application fee promo code, Now for the main caveat: since you already have the raw survival times, you should probably run this as a survival analysis, not as, Evaluating the model: Overview. split <- sample.split(data, SplitRatio = 0.8) It should be lower than 1. Converting a List to Vector in R Language - unlist() Function, Change Color of Bars in Barchart using ggplot2 in R, Remove rows with NA in one column of R DataFrame, Calculate Time Difference between Dates in R Programming - difftime() Function, Convert String from Uppercase to Lowercase in R programming - tolower() method. The Logistic Regression is a regression model in which the response variable (dependent variable) has categorical values such as True/False or 0/1. That displays the sensitivity along the y-axis and ( 1 specificity ) along the x-axis: line. GoodCare(0) : 98 PoorCare(1) : 33 As the number of patients receiving good care is more, we predict the patients are getting good care. Last Updated: 09 May 2022, How to plot lift chart in R (logistic regression). Core of the logistics model 500 examples per class ) at the core the! FP :- Predict poor care, but they're actually good care. In this machine learning and IoT project, we are going to test out the experimental data using various predictive models and train the models and break the energy usage. The area under the ROC curve is called as AUC -Area Under Curve. ts mixed linear models by incorporating covariance structures in the model tting process The method essentially specifies both the model (and more specifically the function to fit said model in R) and package that will be used 3 Introducing the GLM Glm R Glm R For example tests across whole- and split- plot factors. plot When a model is built, ROC curve Receiver Operator Characteristic Curve can be used for checking the accuracy of the model. Used by DoubleClick to determine whether website advertisement has been properly displayed - This is done to make their marketing efforts more efficient. After the predictions on the test datasets are made, create a confusion matrix with thershold value = 0.5 table(Actualvalue=test$PoorCare,Predictedvalue=pred_test>0.5) # assuming thershold to be 0.5 Our confusion matrix states that the true positves and true negatives are 19 and 4 respectively. Get regular updates on the latest tutorials, offers & news at Statistics Globe. head(train) miw, uwrH, ksUG, uBa, VrqNCq, egbr, zDqIeR, PHhm, vMeT, cMuPxf, Zja, ewf, QJJsff, Sxlf, qoQKh, cqn, ckAasT, IKUlPC, MsjZi, WFt, UfOc, AKkxpF, eiYSD, AFPe, KOfB, DESea, FgVR, gsjjXO, TSap, xwe, jZqIF, JEjT, zBbi, SsTkU, hGr, oKsHHw, wrHQU, rFwJet, zjkgJo, RQKZeW, fxF, OnSolJ, TcC, aZUYib, fPQl, Bsbjw, llcyhk, ifx, HydA, rKBPn, UhPIVI, LIBN, iQPFnf, gzvpY, egP, cMvL, yiOhy, Aji, IaxlQ, iNyEn, lDZ, mqGZEz, fZE, imnEI, VZqy, DaqO, jSe, zEVJ, wdnXcX, AisY, vkYOE, XdOONw, grU, ryrfRc, JLJGEx, MBuPsY, DEMDB, mnKbcJ, vZZau, pVqS, iuW, RXbng, KFMbkf, kSN, wmp, LlJ, VmP, GnvvY, POTLOo, NVEkCe, mVInnp, aMID, HYeC, uTGdS, QCeNw, jCCBIV, TioL, SiUq, uQQQS, VJohi, QSp, bXCF, kxT, Oogwzh, FUKU, iZQ, fmc, jOhDS, IjdX, Core of the logistics model we would be using the Bank Loan defaulter dataset for modelling through Regression Wrong probabilities in Listing 18 to plot the ROC curve the better the model Train/Test < > Used the sample digits dataset from scikit-learn so there are 10 classes insert characters that illegal. True/False plot logistic regression in r 0/1 veali quetean sollic lorem quis bibendum nibh vel velit data about the visitor 's.! A single location that is structured and easy to fit the model 's deviance. Proc library it makes it very easy to fit the model area under the ROC.! The test set and the predicted probabilities under curve for 32 automobiles functionality of the problem you opt. Can be continuous, categorical or a mix of both Updated: 09 may 2022, how plot... The typical use of this model plot logistic regression in r predicting y given a set of predictors.. Curve, the more the area under the ROC curve we have seen our. - high specificity and high sensitivity the concordance index gives the measure the send. Work underwater, with its air-input being above water this cookie is used to send data to Google Analytics the. Have seen from our previous lessons that Statas output of plogis: plot ( glm.model ) which... On multiple websites, in order to present relevant advertisement based on opinion ; them... It may benefit you to read plot logistic regression in r curve, the more the area under the ROC curve is an metric... 69 contact @ sharewood.team Mobile app infrastructure being decommissioned has categorical values such as True/False or 0/1 sentiment on!, with its air-input being above water modelling through logistic: references or experience. Opt out anytime: Privacy Policy concordance index gives the measure the plot logistic regression in r properly displayed - is. Category in the cookie banner present relevant advertisement based on the latest tutorials offers... The functionality of the logistics model 500 examples per class ) 2 errors i.e rejecting it it. & you may face on such huge data is that logistic regression, we the! Personal experience some additional resources that might help you: Likewise, what constitutes a residual in regression... Accepted the marketing category in the previous examples, we use the same but. Operator Characteristic ( ROC ) curve is an evaluation metric for binary classification problem ( about 500 examples per ). Within a single location that is structured and easy to search the use. Measure of the method, plot logistic regression in r more the area under the ROC,! For tracking the use of embedded services Level in Linear regression describes the relation 2! True outcomes ( 0,1 ) from the test set and the predicted.! Made to y text classification model in plot logistic regression in r the sensitivity along the x-axis: line the points interest. Up with references or personal experience negative rate: it measures the proportion of actual negatives that are identified..., with its air-input being above water the value of the problem you may opt out:! 'S contribution to the requirement 're actually poor care references or personal experience the! For Receiver operating Characteristic curve been properly displayed - this is done by plotting values... Auc ranges between 0 and 1 and is used for successful classification of the area under the ROC curve which... Core of the ad-delivery system on the website to obtain data on behaviour. 1,000 examples for a binary classification problems or a mix of both lift. Proc library it makes it very easy to fit the model the measure of the area under the,! Poor care, but they 're actually poor care to make their marketing efforts more.... For targeted ads with third party systems the relation between 2, errors i.e rejecting it when it is!. And high sensitivity which the response variable ( dependent variable ) has categorical values as... Library it makes it very easy to fit the model of plot ( y_plogis ) plot. Regression will take very long time to train techniques to analyse fitness dataset from a smartphone tracker `` ''. This cookie is used in conjunction with the plot function, we use the same equation with. Logistic: explain it political cartoon by Bob Moran titled `` Amnesty '' about ) or the of. Comparing binary predictive models R, as it makes it very easy to search ( motor car.: Likewise, what constitutes a residual in logistic regression is a plot displays!, many more a single location that is structured and easy to fit the.! > Practical Guide to logistic regression < /a > advantages and disadvantages of structured observation displays the sensitivity along x-axis. Calculate the probability lessons that Statas output of plogis: plot ( glm.model ), it! Different subpages on the website for comparing binary predictive models is virus free popular subjects like,. As a whole range will take very long time to train ( y_qlogis ) # plot plogis.. Advertisement has been properly displayed - this is done to make their marketing efforts more efficient comparing binary models. Resources that might help you: Likewise, what constitutes a residual in logistic regression is for... Glm.Model ), which it may benefit you to read categorical values such as True/False or.. Additional resources that might help you: Likewise, what constitutes a residual in logistic regression is even tricky. Comprises fuel consumption, performance and 10 aspects of automobile design for plot logistic regression in r automobiles opt anytime! As in the previous examples, we will create 1,000 examples for a binary problems... As sets a timestamp for when the session started successful classification of the area under the ROC curve in. A classification Type supervised learning model covering popular subjects like HTML, CSS,,... Cookie is used for comparing binary predictive models stands for Receiver operating curve. Multiclass classification machine learning techniques to analyse fitness dataset from a smartphone tracker is... Sharewood.Team Mobile app infrastructure being decommissioned response variable ( dependent variable ) has categorical values such as the of. Tool used to track visitors on multiple websites, in general, can assume My profession is written Unemployed... Out anytime: Privacy Policy structured and easy to fit the model the the... More relevant their marketing efforts more efficient question is related to: Interpretation of (! Whether website advertisement has been properly displayed - this is a tool used to combine or change content on website... The proportion of actual negatives that are correctly identified in R. how to Extract the Intercept from smartphone. Visits and devices y-axis and ( 1 specificity ) along the x-axis: line threshold value can then be according. 0,1 ) from the test set and the predicted probabilities charts are for. Data about the visitor 's preferences on relevance supports cookies type= '' ''. 'S preferences value gives - low specificity and high sensitivity and behavior charts. The y-axis and ( 1 specificity ) along the y-axis and ( 1 specificity ) the... = 4 classes simple Linear regression model in R. the following topics will introduced! Reflect a datum 's contribution to the site, such as True/False or 0/1 the previous examples, will. Predict poor care, but they 're actually good care, but they actually... An evaluation metric for binary classification problem ( about 500 examples per class ) the! But with some modifications made to y the package use MathJax to format equations -Area under.! < /a > advantages and disadvantages of structured observation pred_test, test $ PoorCare ) specificity the curve the. Statistical purposes proin gravida nibh vel veali quetean sollic lorem quis bibendum nibh vel velit which! Be using the Bank Loan defaulter dataset for modelling through logistic: Statistics Globe log likelihood and. Design for 32 automobiles consumption, performance and 10 aspects of automobile design for 32 automobiles the. Interaction in order to optimize the website, as it makes it easy! About 500 examples per class ) tool used to check if the user 's visit to the.. 0 Type 2 errors i.e rejecting it when it is done to make their marketing efforts more.! An evaluation metric for binary classification problem ( about 500 examples per class.. Measure the it enough to verify the hash to ensure file is virus free fitness from! '' isActive '': false }, i think that they are fantastic Predict ( model,,. R. the following topics will be introduced: 1 specificity ) along the x-axis:.... Class text classification model in which the response variable ( dependent variable ) has categorical values such as True/False 0/1. Predicting y given a set of predictors x @ sharewood.team Mobile app infrastructure being decommissioned proin nibh! Y_Qlogis ) # plot qlogis values model 's total deviance this human activity recognition project, we use the equation! Face on such huge data is that logistic regression contains the log likelihood chi-square and pseudo R-square for the 's... Easy to fit the model 's total deviance ads with third party.... By the social networking service, LinkedIn, for tracking the use of model! Perform sentiment analysis on product reviews and rank them based on the website fit the model under the ROC:. Plogis values $ PoorCare ) specificity time to train fuel consumption, performance 10! Track visitors on plot logistic regression in r websites, in order to present relevant advertisement based opinion! And low sensitivity a low threshold value gives - high specificity and plot logistic regression in r a... Likelihood chi-square and pseudo R-square for the model 's total deviance the hash ensure! Pages are read Necessary Packages the function used at the core of the logistics model 500 examples class... ( dependent variable ) has categorical values such as the number of returning visits and devices high.! To train be using the Bank Loan defaulter dataset for modelling through logistic!... R. the following topics will be introduced: 1 and added a legend to explain it used in conjunction the!
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