Support vector regression vs support vector machine. html>yw

Apr 22, 2021 · In addition to the above algorithm, support vector regression (SVR) is a useful machine learning algorithms that can be used to solve linear and nonlinear problems 25, especially for small sample In terms of generalization performance, typically the performance differences are minor. Sep 11, 2023 · Support Vector Machines (SVM) It is less sensitive to outliers than other classification algorithms such as k-Nearest Neighbors or Support Vector Machines. Mar 19, 2022 · The support vector machine (SVM) concept was introduced by Vapnik in 1979 [1, 2]. 4. In this task, the output is a real value. The data points on either side of the Feb 26, 2024 · In this article, we are going to discuss the support vector machine in machine learning. SVM will not work well for large amount of data (say a million data points). SVMs or Support Vector Machines are one of the most popular and widely used algorithm for dealing with classification problems in machine learning. They were very famous around the time they were created, during the 1990s, and keep on Support Vector Machine or SVM is one of the most popular Supervised Learning algorithms, which is used for Classification as well as Regression problems. The support vector machine algorithm is a supervised machine learning algorithm that is often used for classification problems, though it can also be applied to regression problems. Linear SVM. It is not used to find the best margin, instead, it can have different decision boundaries with different weights that are near the optimal point. ε-Insensitive Tube on 2-D plot. Jun 24, 2019 · in the classification Epsilon which is the width of yellow area should be maximized. Mar 7, 2021 · Overview. This work compares support vector regression (SVR) with a committee regression technique (bagging) based on regression trees and ridge regression done in feature space and expects that SVR will have advantages in high dimensionality space because SVR optimization does not depend on the dimensionality of the input space. ). Jan 8, 2019 · In Machine Learning, tree-based techniques and Support Vector Machines (SVM) are popular tools to build prediction models. Support Vector Regression is similar to Linear Regression in that the equation of the line is y= wx+b In SVR, this straight line is referred to as hyperplane. This story is part of a deep dive series explaining the mechanics of Machine Learning algorithms. This is part 1 of a 5-sequence, short articles that provide a comprehensive introduction to Support Vector Machines (SVM). T xi + 0): If you take a positive support vector, yi = 1, then. while in regression method it is the opposite the yellow area should be minimized we are trying to find a hyperplane where the yellow area is as narrow as The SVM concepts presented in Chapter 3 can be generalized to become applicable to regression problems, and is characterized by the use of kernels, sparse solution, and VC control of the margin and the number of support vectors. My question: is the difference then between logistic regression (LR) and support vector machines (SVM) is that LR finds any hyperplane that separates the training samples while SVM finds the hyperplane with the maximum margin? Or am I wrong? Support Vector Machine (SVM) SVMs maximize the margin (Winston terminology: the ‘street’) around the separating hyperplane. Support Vector Machine Jun 7, 2018 · Support vector machine is another simple algorithm that every machine learning expert should have in his/her arsenal. The model produced by Support Vector Regression depends only on a subset of the Jul 1, 2020 · Support vector machines are a set of supervised learning methods used for classification, regression, and outliers detection. Dec 5, 2023 · Support Vector Regression (SVR) with a linear kernel: Strengths: Robustness: SVR with a linear kernel is more robust than Linear Regression as it doesn't make as many assumptions. Oct 3, 2020 · Oct 3, 2020. It is a model used for both classification and regression. Support Vector Machine, abbreviated as SVM can be used for both regression and classification tasks. Intro. Jan 23, 2024 · Quantum machine learning endeavors to exploit quantum mechanical effects like superposition, entanglement, and interference to enhance the capabilities of classical machine learning methods. SVR formulates an optimization problem to learn a regression function that Nov 2, 2023 · Nov 2, 2023. As a Supervised Learning Jan 1, 1997 · West Long Branch, NJ 07764. Theoretically, regression by SVMs (SVR) can be very useful due to its ability to find nonlinear, global solutions and its ability to work with high dimensional input vectors. The main objective of this study is to compare the accuracy of allocating new land use transitions based on CA-SVMs approach with conventional coupled logistic The use of Support Vector Regression (SVR) is illustrated including its application to multivariate calibration, and why it is useful when there are outliers and non-linearities. Finally, we mention some modifications and extensions that have been Jul 11, 2020 · Overview of SVR. In classification, we predict a discrete-valued output. So here in this article, we will be covering almost all the necessary things that need to drive for any Jun 3, 2020 · 2. Overall, Logistic Regression is a Dec 20, 2020 · Support Vector Regression. It is an algorithm used for solving classification problems. The model is a hyperplane in the feature space, which in case of classification acts as a boundary, and in case of regression acts as the best-fit line. A unique attribute of SVM is that it operates in feature spaces of increasing dimensionality. Jun 5, 2020 · SVR (Support Vector Regression) is less popular than SVM (Support Vector Machine). The results indicate that the proposed tree-based models could provide accurate H estimation despite of being relatively simple. For instance, it doesn't require the residuals to be normally distributed or homoscedastic (having the same variance at each level of the independent variables). The focus will be to discuss all the doubts we may have when it comes to the similarities and differences between these two algorithms. Bagging, GB, RF and DT) for modeling H and compared their performance with Multi-layer Perceptron (MLP) and Support Vector Regression (SVR). Feb 2, 2023 · Support Vector Machine (SVM) is a relatively simple Supervised Machine Learning Algorithm used for classification and/or regression. We compare support vector regression (SVR) with a committee regression technique (bagging) based on regression trees and ridge regression done in feature space. It tries to find the “best” margin (distance Jan 8, 2013 · Each of the points is colored depending on the class predicted by the SVM; in green if it is the class with label 1 and in blue if it is the class with label -1. where w is a coefficient vector in Dec 5, 2008 · Support vector machine (SVM) algorithms have not yet been studied for prediction of hospital mortality in the Intensive Care Unit (ICU). The use of Support Vector Regression (SVR) is illustrated including its application to multivariate calibration, and why it is useful when there are outliers and non-linearities. Artificial neural networks (ANN) Nov 16, 2023 · Introduction. Basically, SVM finds a hyper-plane that creates a boundary between the types of data. Least-squares support-vector machines (LS-SVM) for statistics and in statistical modeling, are least-squares versions of support-vector machines (SVM), which are a set of related supervised learning methods that analyze data and recognize patterns, and which are used for classification and regression analysis. The main objective of the SVM algorithm is to find the optimal hyperplane in an N-dimensional space that can separate the May 7, 2023 · Support Vector Machine. But, SVR has been proved to be an effective tool in real value function estimation. While linear regression models minimize May 9, 2020 · Logistic Regression (LR) is a probabilistic classification model using the sigmoid function, whereas Support Vector Classifiers (SVC) are a more geometric approach that maximise the margins to each class. It aims to maximize the margin (the distance between the hyperplane and the nearest data points of each class Jan 24, 2012 · Support vector machine (SVM) is a comparatively new machine learning algorithm for classification, while logistic regression (LR) is an old standard statistical classification method. It may provide more accuracy, but may suffer from overfitting. SVM is powerful, easy to explain, and generalizes well in many cases. \end {aligned}$$. In other words, the approach of using SVMs to solve regression problems is called Support Vector Regression or SVR. It is a common misconception that support vector machines are only useful when solving classification problems. There are plenty of algorithms in ML, but still, reception for SVM is always special because of its robustness while dealing with the data. Support Vector Machines : Support vector machine is a supervised learning system and is used for classification and regression problems. Regression is another form of supervised learning. Support Vector Machine (SVM) is a supervised machine learning algorithm used for both classification and regression. 1. Since our goal is a good separation of the two classes, we try to formulate a boundary that leaves as wide a margin as possible between the instances that are closest to it (support vectors), with instances falling into this margin being a possibility, altough Nov 1, 2022 · Artificial neural networks (ANN), gaussian process regression (GPR), support vector machine regression (SVMR), long short-term memory (LSTM), multi-gene genetic programming (MGGP) and M5 model tree (M5Tree) are widely used data-oriented soft computing techniques in civil engineering applications. Oct 4, 2017 · In computational chemistry and chemoinformatics, the support vector machine (SVM) algorithm is among the most widely used machine learning methods for the identification of new active compounds. Linear Support Vector Regression. Methods are illustrated using simulated case studies, and 4 experimental In this study, we make a general comparison of the accuracy and robustness of five multivariate calibration models: partial least squares (PLS) regression or projection to latent structures, polynomial partial least squares (Poly-PLS) regression, artificial neural networks (ANNs), and two novel techniques based on support vector machines (SVMs Jun 4, 2020 · In summary, SVMs pick the decision boundary that maximizes the distance to the support vectors. When doing ridge regression, just like in least-squares SVM, all training instances become Support Vector Machine (SVM): A type of supervised machine learning model used for classification, regression and outliers detection. We will also cover the advantages and disadvantages and application for the same. If the decision boundary is too close to the support vectors then, it will be sensitive to noise and not generalize well. 925%. The SVM, which is a supervised machine learning algorithm was introduced by Vapnik in 1995 to solve classification and regression problems [31]. The sequence assumes no prior knowledge of Machine Learning (ML) and familiarity with Least-squares support vector machine. This tutorial Aug 14, 2020 · Refresh the page, check Medium ’s site status, or find something interesting to read. In this paper, we explore the Aug 15, 2020 · The equation for making a prediction for a new input using the dot product between the input (x) and each support vector (xi) is calculated as follows: f (x) = B0 + sum (ai * (x,xi)) This is an equation that involves calculating the inner products of a new input vector (x) with all support vectors in training data. d. An SVM classifier creates a maximum-margin hyperplane that lies in a transformed input space and splits the example classes while maximizing the distance to the nearest cleanly split examples. May 1 Consider this illustration of a support vector machine used for classification. You can use them to detect cancerous cells based on millions of images or you can use them to predict future driving routes with a well-fitted regression model. However, they can definitely be powerful tools to solve regression problems, yet many people miss this fact. Read more on Difference between Data Science, Machine Learning & AI. The SVM concepts presented in Chapter 3 can be Mar 6, 2018 · This paper presents an approach combining cellular automata (CA) model and support vector machines (SVMs) for modeling urban land use change in Wallonia (Belgium) between 2000 and 2010. Still effective in cases where number of dimensions is greater than the number of samples. (SVR Apr 21, 2023 · Support Vector Regression (SVR) is a type of Support Vector Machine (SVM) algorithms and is commonly used for regression analysis. Support Vector Machines ¶. 432 seconds) La Support Vector Regression (SVR) using linear and non-linear kernels — scikit-learn 1. Recently, bagging and ensemble Support Vector Regression (SVR) is an instance-based, supervised learning algorithm that extends the capabilities of Support Vector Machines (SVM) to regression problems. All of these are common tasks in machine learning. Though we say regression problems as well it’s best suited for classification. Jun 8, 2020 · Support Vector Regression (SVR) ลองเอาข้อมูลที่ใช้ทำ Linear regression จากข้างบน มาทำด้วย SVR ดูบ้าง ในตอนนี้ยังไม่ลงรายละเอียดของการเลือก kernel , epsilon หรือค่า C ละ To get 0, use the complementarity condition for any of the support vectors (in other words, use the fact that the unnormalized margin of the support vectors is one): 1 = yi(. Support vector machines (SVMs) are a set of related methods for supervised learning, applicable to both classification and regression problems. Jun 4, 2020 · For this purpose, SVM is then referred to as Support Vector Machines Regression (SVR). 3 Support vector regression (SVR) SVR is a statistical machine learning method that has been applied in industrial processes. 1. The method cv::ml::SVM::getSupportVectors obtain all of the support vectors. Support vec I also know that Support vector machines finds the hyperplane with the maximum margin. The SVM is one of the supervised learning models that investigate data and identifies data samples used for classification. 5. we are trying to find the hyperplane where the yellow area is as wide as possible. 2. to separate 2 classes. Nov 1, 2021 · In this study, various soft-computing models (Gaussian process regression (GPR) and support vector machines (SVM) based on the polynomial kernel function (PKF), Pearson VII universal kernel function (PUKF), and radial basis kernel function (RBKF), as well as pruned/unpruned M5P tree models) were simultaneously applied for the first time in prediction of the lateral confinement coefficient (K s In this study, an alternative approach based on support vector machines (SVMs) is used, the least squares support vector machine (LS-SVM) regression. 0 = 1 T xi: Written another way, since the support vectors have the smallest margins, Dec 31, 2022 · Linear regression performs well on linear data, it can not handle the outliers properly, and the regularization techniques can be easily implemented. Let's discuss them one by one. Also, as it comes from the family of GLMs, there are statistical tools available to understand the relationships between the predictors and the target, and, most importantly, to formally statistically Jul 30, 2023 · The extension for continuous outcomes is called support vector regression. SVMs were developed in the 1990s by Vladimir N. The decision function is fully specified by a (usually very small) subset of training samples, the support vectors. However, primarily, it is used for Classification problems in Machine Learning. Hence, SVM conceptually departs May 22, 2024 · SVM is a powerful supervised algorithm that works best on smaller datasets but on complex ones. Abstract. This algorithm acknowledges the presence of non-linearity in the data and provides a proficient prediction model. Support Vector Machine (SVM) is a very popular Machine Learning algorithm that is used in both Regression and Classification. Regression. The SVM algorithm as a relatively new classification or prediction method, has been developed by Vapnik et al. , cliff vs noncliff) and potency differences, respectively. Looking at the scatter plot by two features X1, X2 as below. SVR is widely used in various fields, including finance, healthcare, and engineering. The idea of Support Vector Regression has been borrowed from Support Vector Machines. Support Vector Machine, abbreviated as SVM can be used for both regression and classification tasks, but generally, they work best in classification problems. The purpose of using SVMs for regression problems is to define a hyperplane as in the…. The model with the closest performance to support vector machines was quadratic discriminant with a misclassification ratio of 12. On the basis of these experiments, it is expected that SVR will have advantages in high dimensionality space because SVR optimization does not depend on the dimensionality of the input Mar 19, 2022 · The support vector machine (SVM) algorithm is one of the most widely used machine learning (ML) methods for predicting active compounds and molecular properties. In Gaussian linear regression, we minimize a quadratic loss function: $$\displaystyle \begin {aligned} L (y_i,f (x_i))= (y_i-f (x_i))^2. Jan 5, 2020 · SVM is a more complex model (non-linear model) than logistic regression (linear model). Jun 12, 2024 · Support Vector Regression uses the same principle of Support Vector Machines. For simplicity, I’ll focus on binary classification problems in this article. , 1997, Vapnik, 1998 ). Researchers have now developed quantum support vector classifiers and substantiated Apr 15, 2020 · In machine learning and artificial intelligence, an important type of problem is called classification. Mar 18, 2024 · In this tutorial, we’ll briefly introduce support vector machine and perceptron algorithms. in the 1990s [2–4] as a result of the collaboration between the statistical and the machine learning research community. Vapnik and his colleagues, and they published this work in a paper titled "Support Jan 14, 2022 · The support vector regression (SVR) is inspired by the support vector machine algorithm for binary response variables. As for classification the regression function only depends on a subset of the training data. As in classification, support vector regression (SVR) is characterized by Sep 26, 2016 · By applying these concepts, ACs were encoded as single descriptor vectors used as input for support vector machine (SVM) classification and support vector regression (SVR), yielding accurate predictions of AC status (i. It is more preferred for classification but is sometimes very useful for regression as well. • This paper presented an improved support vector regression that can tackle with noisy data. In this algorithm, the data points are first represented in an n-dimensional space. For the time being, we will use a linear kernel and set the C parameter to a very large number (we'll discuss the meaning of these in more depth momentarily). Generate sample data: Fit regression model: Look at the results: Total running time of the script:(0 minutes 0. Given data points, it attempts to find a curve. Let’s start from Linear SVM that is known as SVM without kernels. See Can support vector machine be used in large data? Support Vector Regression The key to artificial intelligence has always been the representation. • The constraints of the standard SVR are converted to fuzzy Sep 5, 2017 · Support vector machine (SVM) is a supervised machine learning method which can be used for both classification and regression models. Jul 26, 2023 · Support Vector Machine vs Logistic Regression - Introduction While SVM excels in cases requiring clear separation margins or nonlinear decision boundaries while coping well even with limited samples, LR shines when simplicity meets model interpretability requirements within binary classification tasks. Jan 1, 2020 · Support vector regression (SVR) is a supervised machine learning technique to handle regression problems ( Drucker et al. The working of a support vector machine involves constructing a hyperplane or set of hyperplanes in a high-dimensional space, which can be used for classification, regression, or other tasks like outliers detection. • The performance of SVR is affected by noisy data points in training samples. As we have discussed above, the TLDR. Support Vector Machines. So, QC + SVM methodology is an alternative to QC + ANN one. Support Vector Machines are powerful supervi. Then we’ll explain the differences between them, and how to use them. The setup is somewhat different than for SVC. One of the most popular use cases of SVR is in stock price prediction. Logistic regression is ideal for classification problems with pre-identified independent variables. They are similar in that they both can divide the feature space with a decision boundary. The decision boundary is drawn in a way that the distance to support vectors are maximized. SVM, on the other hand, is more effective with unstructured data such as text and images and has a lower risk of overfitting. Generally, Support Vector Machines is considered to be a classification approach, it but can be employed in both types of classification and regression problems. “A Support Vector Machine (SVM) is a powerful machine learning algorithm used primarily for classification and regression tasks. vectors is introduced. We first need to define a loss function. Regression analysis is useful to analyze the relationship between a dependent variable and one or more predictor variables. This becomes a Quadratic programming problem that is easy to solve by standard methods. Support Vector Machine (SVM) is one of the Machine Learning (ML) Supervised algorithms. In addition, support vector regression (SVR) has become a preferred approach for modeling nonlinear structure–activity relationships and predicting compound potency values. Mar 1, 2021 · Support vector regression (SVR) is a powerful method for regression and curve fitting applications. Fitting a support vector machine¶ Let's see the result of an actual fit to this data: we will use Scikit-Learn's support vector classifier to train an SVM model on this data. Aug 1, 2004 · Abstract. It can easily handle multiple continuous and categorical variables. The approach was originally designed for binary object classification and then adapted for the prediction of numerical values (termed support vector regression, SVR). However, the use of SVMs in regression is not very well documented. SVM works by finding a hyperplane in a high-dimensional space that best separates data into different classes. [31] explored the potential of four tree-based methods (i. Support Vector Regression maintains all the interesting properties from Support Vector Machines (Saed Sayad, n. Seen this way, support vector machines belong to a natural class of algorithms for statistical inference, and many of its unique features are due to the behavior of the hinge loss. May 20, 2016 · With respect to logistic regression, at the top of its list of advantages is that it will produce a model that is considerably more explainable. We compare support vector regression. Jun 12, 2024 · A Support Vector Machine (SVM) is a supervised machine learning algorithm used for classification and regression tasks. The objective of the series is to help you thoroughly understand SVM and be able to confidently use it in your own projects. Decision trees and SVM can be intuitively understood as classifying different groups (labels), given their theories. Expand. The goal of the SVM algorithm is to create the best line or decision boundary that can segregate n-dimensional Feb 27, 2022 · In conclusion, both logistic regression and support vector machine have their strengths and weaknesses. . Machine Learning is making huge leaps forward, with an increasing number of algorithms enabling us to solve complex real-world problems. A new regression technique based on Vapnik’s concept of support. This article describes and compares four of the most commonly used classification techniques: logistic regression, perceptron, support vector machine (SVM), and single hidden layer neural networks. Here are some things to note: As the name suggests Support Vector Regression is used for predicting the real-valued output. **Bell Labs +AT&T Labs. It can overfit and underfit the data if not tuned well. In chemoinformatics and drug discovery, SVM has been a state-of-the-art ML approach for more than a decade. Image by author. 10 . The advantages of support vector machines are: Effective in high dimensional spaces. Owing to its robustness, it’s generally implemented for solving classification tasks. In this paper, we introduce a new model of SVM and support vector regression which any of training samples containing inputs and outputs are considered the random variables with known or unknown probability functions. Mar 30, 2022 · What are Support Vector Machines? Support Vector Machines or SVMs have supervised learning algorithms that can be used with both regression and classification tasks. Rooted in statistical learning or Vapnik-Chervonenkis (VC) theory, support vector machines (SVMs) are well positioned to generalize on yet-to-be-seen data. l }, where x i ∈ RN, yi ∈ R, SVR aims at finding a regression function that can fit all training samples, (3) f x = w T Φ x + b. We use here a couple of methods to obtain information about the support vectors. It is a variant of Support Vector Machines (SVM) and is designed to predict continuous Regression Overview CLUSTERING CLASSIFICATION REGRESSION (THIS TALK) K-means •Decision tree •Linear Discriminant Analysis •Neural Networks •Support Vector Machines •Boosting •Linear Regression •Support Vector Regression Group data based on their characteristics Separate data based on their labels Find a model that can explain Nov 20, 2020 · Support Vector Machine (SVM) Algorithm. Support Vector Regression is a machine learning model that uses the Support Vector Machine, a classification algorithm, to predict a continuous variable. One of the most researched quantum machine learning methodologies presently is the quantum support vector machine (QSVM). In this article, I’ll explain the rationales behind SVM and show the implementation in Python. A linear algorithm is used in the kernel-induced feature space to construct a function such that the training points are inside a tube of given radius ε. For the closely related Feb 26, 2024 · Support vector machines are powerful machine learning algorithms used for classification and, in some cases, regression tasks. In this tutorial, you’ll learn about Support Vector Machines (or SVM) and how they are implemented in Python using Sklearn. In this tutorial we give an overview of the basic ideas underlying Support Vector (SV) machines for function estimation. May 15, 2018 · Hassan et al. e. For a training set T= { (X i, yi ), i = 1…. The SVM Oct 28, 2004 · Partial Least Squares (PLS) is the most used regression technique for this task whereas Support Vector Machines (SVMs) are hardly known and used in chemometrics. SVMs are powerful supervised learning algorithms that are May 28, 2021 · The support vector machine (SVM) and minimum Euclidean norm least squares regression are two fundamentally different approaches to fitting linear models, but they have recently been connected in models for very high-dimensional data through a phenomenon of support vector proliferation, where every training example used to fit an SVM becomes a support vector. In this new models, we need the mathematical 2. The SVM concepts presented in Chapter 3 can be generalized to become applicable to regression problems. Decision performs well on nonlinear data but is prone to minor changes in the dataset. Support vector machine is one of the complex Jul 4, 2024 · Support Vector Machine. Support Vector Regression (SVR) is a machine learning technique used for regression tasks. 725% and 2. Dec 27, 2023 · A support vector machine (SVM) is a supervised machine learning algorithm that classifies data by finding an optimal line or hyperplane that maximizes the distance between each class in an N-dimensional space. Oct 15, 2018 · Continuing this journey, I have discussed the loss function and optimization process of linear regression at Part I, logistic regression at part II, and this time, we are heading to Support Vector Machine. 9. Hence, support vector machine showed a superior performance over other algorithms. It has been applied to ab initio (first principle) and density functional theory (DFT) quantum chemistry data. Support vector machines (SVMs) are a set of supervised learning methods used for classification , regression and outliers detection. The increasing interest in Support Vector Machines (SVMs) over the past 15 years is described. However, rather than having the curve act as a decision boundary in a classification problem, in SVR, a match is found between some vector and the position on the curve. —Jeff Hawkins Rooted in statistical learning or Vapnik-Chervonenkis (VC) theory, support vector machines (SVMs) are well positioned to generalize on yet-to-be-seen data. Now let us look at the classic example of the Boston House Price dataset. The soft-margin support vector machine described above is an example of an empirical risk minimization (ERM) algorithm for the hinge loss. The main idea of the algorithm consists of only using residuals smaller in absolute value than some constant (called ε -sensitivity), that is, fitting a tube of ε width to the data, as illustrated in Fig. SVM constructs a hyperplane in multidimensional space to separate different classes. View Paper. Support vector machine is highly preferred by many as it produces significant accuracy with less computation power. A SV algorithm for regression, called support vector regression (SVR), was proposed in 1996 . That said, minimizing the l1 l 1 -norm has the extremely attractive feature of yielding sparse solutions (the support vectors are a subset of the training set). Furthermore, we include a summary of currently used algorithms for training SV machines, covering both the quadratic (or convex) programming part and advanced methods for dealing with large datasets. --. 1 documentation Apr 27, 2015 · Abstract. Sep 1, 2023 · Support vector machine (SVM) for regression. Although there have been many comprehensive studies comparing SVM and LR, since they were made, there have been many new improvements applied to them such as bagging and ensemble. Feb 25, 2022 · February 25, 2022. Methods are illustrated using simulated case studies, and 4 experimental Dec 10, 2019 · For the detail explanation, you can read this one “ Support Vector Machine: Classification ”. Aug 18, 2023 · Abstract. A note about the Soft margin and Jul 31, 2019 · Support Vector Machine (SVM) is probably one of the most popular ML algorithms used by data scientists. Two support vector machines with different settings have the lowest misclassification ratio of 5. 5%. fs ba yp vu nq yw es om so sb