How Does Svm Works? 1. Linearly Separable Data . Let us understand the working of SVM by taking an example where we have two classes that are shown is the below image which are a class A: Circle & class B: Triangle. Now, we want to apply the SVM algorithm and find out the best hyperplane that divides the both classes ** The goal of the SVM algorithm is to create the best line or decision boundary that can segregate n-dimensional space into classes so that we can easily put the new data point in the correct category in the future**. This best decision boundary is called a hyperplane. SVM chooses the extreme points/vectors that help in creating the hyperplane

Here are the steps regularly found in machine learning projects: Import the dataset; Explore the data to figure out what they look like; Pre-process the data; Split the data into attributes and labels; Divide the data into training and testing sets; Train the SVM algorithm; Make some predictions; Evaluate the results of the algorithm In the SVM algorithm, we plot each data item as a point in n-dimensional space (where n is number of features you have) with the value of each feature being the value of a particular coordinate. Then, we perform classification by finding the hyper-plane that differentiates the two classes very well (look at the below snapshot) According to SVM, we have to find the points that lie closest to both the classes. These points are known as support vectors. In the next step, we find the proximity between our dividing plane and the support vectors. The distance between the points and the dividing line is known as margin. The aim of an SVM algorithm is to maximize this very margin SVM is a supervised machine learning algorithm that is commonly used for classification and regression challenges. Common applications of the SVM algorithm are Intrusion Detection System, Handwriting Recognition, Protein Structure Prediction, Detecting Steganography in digital images, etc. In the SVM algorithm, each point is represented as a data. In the SVM algorithm, we plot each observation as a point in an n-dimensional space (where n is the number of features in the dataset). Our task is to find an optimal hyperplane that successfully classifies the data points into their respective classes

Gradient (or steepest) descent algorithm for SVM First, rewrite the optimization problem as an average min w C(w)= λ 2 ||w||2 + 1 N XN i max(0,1 −yif(xi)) = 1 N XN i µλ 2 ||w||2 +max(0,1 −yif(xi)) ¶ (with λ=2/(NC) up to an overall scale of the problem) and f(x)=w>x + b Because the hinge loss is not diﬀerentiable, a sub-gradient is compute 6. Set your algorithm to SVM. Go to settings and make sure you select the SVM algorithm in the advanced section. 7. Test Your Classifier. Now you can test your SVM classifier by clicking on Run > Demo. Write your own text and see how your model classifies the new data: 8. Integrate the topic classifie The main goal of SVM is to divide the datasets into classes to find a maximum marginal hyperplane (MMH) and it can be done in the following two steps −. First, SVM will generate hyperplanes iteratively that segregates the classes in best way. Then, it will choose the hyperplane that separates the classes correctly Support Vector Machine Libraries / Packages: For implementing support vector machine on a dataset, we can use libraries. There are many libraries or packages available that can help us to implement SVM smoothly. We just need to call functions with parameters according to our need. In Python, we can use libraries like sklearn SVM is one of the most popular, versatile supervised machine learning algorithm. It is used for both classification and regression task.But in this thread we will talk about classification task

- The rest of the
**steps**are typical machine learning**steps**and need very little explanation until we reach the part where we train our Kernel**SVM**. Importing Libraries Importing the Datase - In this section, we will be training and evaluating models based on each of the algorithms that we considered in the last part of the Classification series— Logistic regression, KNN, Decision Tree Classifiers, Random Forest Classifiers, SVM, and Naïve Bayes algorithm. The following will be the criterion for comparison of the algorithms
- Introduction. Support vector machine (SVM) is another very popular machine learning algorithm, which belongs to the supervised learning class, and can be used for both regression and classification purposes. The support vector machine's main purpose is to create a line, best known as a hyperplane (decision boundary), that can separate the data.
- SVM is an algorithm that takes the data as an input and outputs a line that separates those classes if possible. Lets begin with a problem. Suppose you have a dataset as shown below and you need to classify the red rectangles from the blue ellipses (let's say positives from the negatives)
- According to the SVM algorithm we discover the points closest to the road from both the classes. These points are called support vectors. Decision boundaries in SVM are the two lines that we see alongside the hyperplane. The distance between the two light-toned lines is called the margin
- g optimization problem into smaller problems and is very effective at solving SVMs. But, SMO is rather complicated and this example strives for simplicity. The Pegasos algorithm [5] is much simpler and uses stochastic gradient descent (SGD) with a variable step size
- Yes there is: the practical guide to SVM classification by the LIBSVM authors. Edit: the pdf I linked shows how to use SVM, not how to implement them. If you don't understand the math, I don't recommend attempts at implementing it (many have tried, few have succeeded). Long story short: the easiest solver you could implement is a general quadratic.

-The resulting learning algorithm is an optimization algorithm rather than a greedy search Organization •Basic idea of support vector machines: just like 1-layer or multi-layer neural nets -Optimal hyperplane for linearly separable patterns -Extend to patterns that are not linearly separable by transformations of original data t Enter The World Of Computer Vision! OpenCV For Beginners | Official OpenCV Course - http://bit.ly/OpenCVKickStarter --~--Want to learn what make Support Vect.. Follow my podcast: http://anchor.fm/tkortingIn this video I explain how SVM (Support Vector Machine) algorithm works to classify a linearly separable binary. The second step is the image segmentation which is applied with the region based segmentation called k-mean segmentation. The algorithm of GLCM is applied for the textual feature analysis in the step number four. In the last step, the SVM classification algorithm is applied for the fruit quality assessment Support Vector Machines: First Steps¶. Kernel-based learning algorithms such as support vector machine (SVM, [CortesVapnik1995]) classifiers mark the state-of-the art in pattern recognition .They employ (Mercer) kernel functions to implicitly define a metric feature space for processing the input data, that is, the kernel defines the similarity between observations

* SVM is just one among many models you can use to learn from this data and make predictions*. Note that the crucial part is Step 2. If you give SVM unlabeled emails, then it can do nothing. SVM learns a linear model Now we saw in our previous example that at the Step 3 a supervised learning algorithm such as SVM is trained with the labeled data. Svm classifier implementation in python with scikit-learn. Support vector machine classifier is one of the most popular machine learning classification algorithm. Svm classifier mostly used in addressing multi-classification problems. If you are not aware of the multi-classification problem below are examples of multi-classification problems Support Vector Machine or SVM is a supervised and linear Machine Learning algorithm most commonly used for solving classification problems and is also referred to as Support Vector Classification. There is also a subset of SVM called SVR which stands for Support Vector Regression which uses the same principles to solve regression problems

* A support vector machine (SVM) is a supervised learning algorithm used for many classification and regression problems , including signal processing medical applications, natural language processing, and speech and image recognition*.. The objective of the SVM algorithm is to find a hyperplane that, to the best degree possible, separates data points of one class from those of another class Although the class of algorithms called SVMs can do more, in this talk we focus on pattern recognition. So we want to learn the mapping: X7!Y,wherex 2Xis some object and y 2Yis a class label. Let's take the simplest case: 2-class classiﬁcation. So: x 2 Rn, y 2f 1g

Such data points are termed as non-linear data, and the classifier used is termed as a Non-linear SVM classifier. Algorithm for Linear SVM. Let's talk about a binary classification problem. The task is to efficiently classify a test point in either of the classes as accurately as possible. Following are the steps involved in the SVM process A support vector machine is a Classification method. supervised algorithm used for: Classification and Regression (binary and multi- class problem) anomalie detection (one class problem) Supports: text mining. nested data problems e.g. transaction data or gene expression data analysis. pattern recognition

step, both the algorithms spend their maximum effort in ﬁnd-ing the maximum violator. If S is the current candidate Sup-port Vector set, and n the size of the dataset, the algorithms Recently some work has also been done on incremental SVM algorithms which can converge to exact solutions and also efﬁciently calculate leave one out. These, two vectors are support vectors. In SVM, only support vectors are contributing. That's why these points or vectors are known as support vectors.Due to support vectors, this algorithm is called a Support Vector Algorithm(SVM).. In the picture, the line in the middle is a maximum margin hyperplane or classifier.In a two-dimensional plane, it looks like a line, but in a multi-dimensional. مثال1 : SVM مع Linear Kernal استيرادالمكتبات الضرورية import numpy as np import matplotlib.pyplot as plt from sklearn import svm, datasets استيراد البيانات iris = datasets.load_iris() X = iris.data[:, :2] y = iris.targe Support Vector Machines ( SVM ) 1. Support Vector Machine Classification , Regression and Outliers detection Khan 2. Introduction SVM A Support Vector Machine (SVM) is a discriminative classifier which intakes training data (supervised learning), the algorithm outputs an optimal hyperplane which categorizes new examples * Understanding Support Vector Machines*. SVM are known to be difficult to grasp. Many people refer to them as black box. This tutorial series is intended to give you all the necessary tools to really understand the math behind SVM. It starts softly and then get more complicated

- (ii) methods with slow asymptotic convergence and cheap steps, requiring only (approximate) function / gradient information. The latter may be more appealling when we need only an approximate solution. The best algorithms may combine both approaches. Stephen Wright (UW-Madison) Optimization in SVM Comp Learning Workshop 10 / 5
- Each group has 50 images (150 images total). Use LBP to prepare the 150 images for classification. Train a SVM on 150 LBP images with labels: 0: Child. 1: Young Adult. 2: Senior. Test the system using a set of new images. If all goes according to plan, the system should properly classify images based on the groups defined in step 2. The algorithm
- SVM ALGORITHM SVM.py: Input: document-term matrix; Output: trained model and predictions with model; Overview: Contains an svm class use to build, train and predict a given data set
- Photo by Sam Burriss on Unsplash. In this article, we will learn to use Principal Component Analysis and Support Vector Machines for building a facial recognition model.. First, let us understand what PCA and SVM are:. Principal Component Analysis: Principal Component Analysis (PCA) is a machine learning algorithm that is widely used in exploratory data analysis and for making predictive models
- ative classifier. SVM finds an optimal hyperplane which helps in classifying new data points
- There are many different machine learning algorithms we can choose from when doing text classification with machine learning.One of those is Support Vector Machines (or SVM).. In this article, we will explore the advantages of using support vector machines in text classification and will help you get started with SVM-based models with MonkeyLearn.. From Texts to Vector

Implementing the SVM algorithm (Hard margin)¶ Case 1) Linearly separable, binary classification ¶ Using the notation and steps provided by Tristan Fletcher the general steps to solve the SVM problem are the following Support Vector Machines are perhaps one of the most popular and talked about machine learning algorithms. They were extremely popular around the time they were developed in the 1990s and continue to be the go-to method for a high-performing algorithm with little tuning. In this post you will discover the Support Vector Machine (SVM) machine learning algorithm the standard SVM training algorithm. The new SVM learning algorithm is called Sequential Minimal Optimization (or SMO). Instead of previous SVM learning algorithms that use numerical quadratic programming (QP) as an inner loop, SMO uses an analytic QP step. This paper first provides an overview of SVMs and a review of current SVM training.

Then k-means algorithm is applied which can help to clot the clusters. The following system is a software based solution for detecting the disease with which the leaf is infected. In order to detect the disease some steps are to be followed using image processing and support vector machine The traditional classification algorithm of protein secondary structure is a one-step classification algorithm, which directly substitute the feature vectors obtained through protein secondary structural sequences into the support vector machine (SVM) algorithm, and then obtained the results of four classes * Sequential minimal optimization (SMO) is an algorithm for solving the quadratic programming (QP) problem that arises during the training of support-vector machines (SVM)*. It was invented by John Platt in 1998 at Microsoft Research. SMO is widely used for training support vector machines and is implemented by the popular LIBSVM tool. The publication of the SMO algorithm in 1998 has generated a.

The resulting, trained model (SVMModel) contains the optimized parameters from the SVM algorithm, enabling you to classify new data. For more name-value pairs you can use to control the training, see the fitcsvm reference page. Classifying New Data with an SVM Classifier. Classify new data using predict break_ties bool, default=False. If true, decision_function_shape='ovr', and number of classes > 2, predict will break ties according to the confidence values of decision_function; otherwise the first class among the tied classes is returned.Please note that breaking ties comes at a relatively high computational cost compared to a simple predict Support Vector Machine (SVM) is one of the most powerful out-of-the-box supervised machine learning algorithms. Unlike many other machine learning algorithms such as neural networks, you don't have to do a lot of tweaks to obtain good results with SVM The following are the steps to make the classification: Import the data set. Make sure you have your libraries. The e1071 library has SVM algorithms built in. Create the support vectors using the library. Once the data is used to train the algorithm plot, the hyperplane gets a visual sense of how the data is separated

algorithm for solving the optimization problem cast by Support Vector Machines (SVM). We prove that the number of iterations required to obtain a solution of accuracy is O˜(1/ ), where each iteration operates on a single training example ** A Hardware-Efﬁcient ADMM-Based SVM Training Algorithm for Edge Computing Shuo-An Huang, Student Member, IEEE and Chia-Hsiang Yang, Senior Member, IEEE Abstract—This work demonstrates a hardware-efﬁcient sup-port vector machine (SVM) training algorithm via the alternative direction method of multipliers (ADMM) optimizer**. Low-ran

A Support Vector Machine, or SVM, is a non-parametric supervised learning model. For non-linear classification and regression, they utilise the kernel trick to map inputs to high-dimensional feature spaces. SVMs construct a hyper-plane or set of hyper-planes in a high or infinite dimensional space, which can be used for classification, regression or other tasks. Intuitively, a good separation. Let us look at the libraries and functions used to implement SVM in Python and R. Python Implementation. The most widely used library for implementing machine learning algorithms in Python is scikit-learn. The class used for SVM classification in scikit-learn is svm.SVC() sklearn.svm.SVC (C=1.0, kernel='rbf', degree=3, gamma='auto' SVM::OPT_GAMMA. Algorithm parameter for Poly, RBF and Sigmoid kernel types. SVM::OPT_NU. The option key for the nu parameter, only used in the NU_ SVM types. SVM::OPT_EPS. The option key for the Epsilon parameter, used in epsilon regression. SVM::OPT_P. Training parameter used by Episilon SVR regression

- A support vector machine (SVM) is machine learning algorithm that analyzes data for classification and regression analysis. SVM is a supervised learning method that looks at data and sorts it into one of two categories. An SVM outputs a map of the sorted data with the margins between the two as far apart as possible
- The PCA/SVM-based method can be used to solve the detection problem inherent when the data from multiple sensors contain imperfections in the form of imprecision, uncertainty, and incoherence. The system combined the PCA/feature-based algorithm with an SVM for detecting patterns
- ative classifier formally defined by a separating hyperplane. In other words, given labeled training data ( supervised learning ), the algorithm outputs an optimal hyperplane which categorizes new examples
- SVM Accuracy Score -> 84.6% Finishing Up. In conclusion, I hope this has explained what text classification is and how it can be easily implemented in Python. As a next step you can try the following: Play around with the Data preprocessing steps and see how it effects the accuracy. Word Vectorization techniques such as Count Vectorizer and.
- SVM is a supervised machine learning algorithm which can be used for classification or regression problems. It uses a technique called the kernel trick to transform your data and then based on these transformations it finds an optimal boundary between the possible outputs. Simply put, it does some extremely complex data transformations, then.
- g problem in the dual space, it can be built fast by solving the primal optimization problem also. In this article a Support Vector Machine implementation is going to be described by solving the primal optimization problem with sub-gradient solve
- This Machine learning Image classification uses scikit-learn SVM image classification algorithm. Open the google collab file and follow all the steps. You can classify any category images

Advantages: This algorithm requires a small amount of training data to estimate the necessary parameters. Naive Bayes classifiers are extremely fast compared to more sophisticated methods. Disadvantages: Naive Bayes is is known to be a bad estimator. Steps for Implementation: Initialise the classifier to be used between the two classes. The input to a SVM algorithm is a set {( XI, Yi) } of labeled training data, where XI is the data and Yi = -1 or 1 is the label. The output of a SVM algorithm is a set of Ns support vectors SI, coefficient weights ai, class labels Yi of the support vectors, and a constant term b. The linear decision surface is wher On the basis of previous studies, a new idea of classification **algorithm** for protein structural classes is proposed in this paper. By constructing a **step**-by-**step** classification **algorithm** based on double-layer **SVM** model, this method breaks the traditional pattern of using **SVM** **algorithm** only and also breaks the limitations of **SVM** **algorithm** This is a guide to KNN Algorithm. Here we discuss the introduction and working of the K Nearest Neighbours algorithm with steps to implement the kNN algorithm in python. You may also look at the following articles to learn more-How does SVM Algorithm Works? MD5 Algorithm (Advantages and Disadvantages) K- Means Clustering Algorithm

This paper focuses on the application of machine learning algorithms for predicting spinal abnormalities. As a data preprocessing step, univariate feature selection as a filter based feature selection, and principal component analysis (PCA) as a feature extraction algorithm are considered. A number of machine learning approaches namely support vector machine (SVM), logistic regression (LR. Time series classification is a basic and important approach for time series data mining. Nowadays, more researchers pay attention to the shape similarity method including Shapelet-based algorithms because it can extract discriminative subsequences from time series. However, most Shapelet-based algorithms discover Shapelets by searching candidate subsequences in training datasets, which brings. Breast cancer is an all too common disease in women, making how to effectively predict it an active research problem. A number of statistical and machine learning techniques have been employed to develop various breast cancer prediction models. Among them, support vector machines (SVM) have been shown to outperform many related techniques. To construct the SVM classifier, it is first necessary.

During first step the model is created by applying classification algorithm on training data set then in second step the extracted model is tested against a predefined test data set to measure the model trained performance and accuracy. So classification is the process to assign class label from data set whose class label is unknown. ID3 Algorithm A step-by-step classification algorithm of protein secondary structures based on double-layer SVM model. Ge Y(1), Zhao S(2), Zhao X(3). Author information: (1)School of Mathematical Sciences, Ocean University of China, Qingdao 266100, PR China. (2)College of Information Science and Engineering, Ocean University of China, Qingdao 266100, PR China In this paper, a step-by-step classification algorithm based on double-layer SVM model is constructed to predict the secondary structure of proteins. The most important feature of this algorithm is to improve the prediction accuracy of α+β and α/β classes through transforming the prediction of two c The main goal of SVM is to divide the datasets into classes to find a maximum marginal hyperplane (MMH) and it can be done in the following two steps −. First, SVM will generate hyperplanes iteratively that segregates the classes in best way. Then, it will choose the hyperplane that separates the classes correctly. Implementing SVM in Pytho

- But fortunately, the SVM algorithm provides us with techniques such as kernels that can do this job for us. Kernels do some complex data transformations which help to convert the linearly inseparable problems to separable problems for classifying the data into classes given by us
- This paper presents a fast online support vector machine (FOSVM) algorithm for variable-step CDMA power control. The FOSVM algorithm distinguishes new added samples and constructs current training sample set using K.K.T. condition in order to reduce the size of training samples. As a result, the training speed is effectively increased
- imum with these steps, we're going to step down to a 1% step size to continue finding the
- g problem with linear constriants
- On the basis of previous studies, a new idea of classification algorithm for protein structural classes is proposed in this paper. By constructing a step-by-step classification algorithm based on double-layer SVM model, this method breaks the traditional pattern of using SVM algorithm only and also breaks the limitations of SVM algorithm
- In the case of linear SVM all the alpha's will be 1's. svidx: the optional output vector of indices of support vectors within the matrix of support vectors (which can be retrieved by SVM::getSupportVectors). In the case of linear SVM each decision function consists of a single compressed support vector

7. Stochastic Gradient Descent Algorithm. Stochastic Gradient Descent (SGD) is a class of machine learning algorithms that is apt for large-scale learning.. It is an efficient approach towards discriminative learning of linear classifiers under the convex loss function which is linear (SVM) and logistic regression.. We apply SGD to the large scale machine learning problems that are present in. SVM Classifier splits the data into two classes using a hyperplane which is basically a line that divides a plane into two parts. Applications of Support Vector Machine in Real Life. As you already know Support Vector Machine (SVM) based on supervised machine learning algorithms, so, its fundamental aspire to classify the concealed data SVM Hyperparameter Tuning using GridSearchCV | ML. A Machine Learning model is defined as a mathematical model with a number of parameters that need to be learned from the data. However, there are some parameters, known as Hyperparameters and those cannot be directly learned. They are commonly chosen by human based on some intuition or hit and. **SVM** approach to fully automatic, unobtrusive expression recognition in live video. Facial Expression Recognition using Support Vector Machines Philipp Michel & Rana El Kaliouby Our approach makes no assumptions about the specific emotions used for training or classification and works for arbitrary, user-defined emotion categories

- The support vector machine searches for the closest points (Figure 2), which it calls the support vectors (the name support vector machine is due to the fact that points are like vectors and that the best line depends on or is supported by the closest points). Once it has found the closest points, the SVM draws a line connecting them.
- Support Vector Machine algorithms are not scale invariant, so it is highly recommended to scale your data. For example, scale each attribute on the input vector X to [0,1] or [-1,+1], or standardize it to have mean 0 and variance 1. Note that the same scaling must be applied to the test vector to obtain meaningful results
- As the name suggests, machine learning is the ability to make machines learn through data by using various machine learning algorithms, and in this blog, we'll discuss how the SVM algorithm.

To solve this optimization problem, SVM multiclass uses an algorithm that is different from the one in [1]. The algorithm is based on Structural SVMs [2] and it is an instance of SVM struct. For linear kernels, SVM multiclass V2.20 is very fast and runtime scales linearly with the number of training examples. Non-linear kernels are not (really. In the next step, the SVM algorithm seeks to identify the optimal margin between the support vectors and the dividing hyperplane, called the margin. The SVM algorithm seeks to maximize the margin As shown, the SSD-SVM algorithm yields higher sensitivity results than the PSO-SVM and BA-SVM algorithms in most cases. Moreover, using the PSO-SVM algorithm, the p values for D1 and D5 are larger than the predicted statistical significance level of 0.005, but the other p values are smaller than the significance level of 0.005

Support Vector Machine (SVM) is a supervised machine learning algorithm that can be used for both classification or regression problems. SVM is one of the most popular algorithms in machine learning and we've often seen interview questions related to this being asked regularly In this article, we will learn how to use an SVM (Support Vector Machine) to build and train a model using human cell records and classify cells as to whether the samples are benign or malignant. Support Vector Machines (SVM) Support Vector Machines (SVM) is one of the most popular Machine Learning algorithms used for classification

- Overview. SVM rank is an instance of SVM struct for efficiently training Ranking SVMs as defined in [Joachims, 2002c]. SVM rank solves the same optimization problem as SVM light with the '-z p' option, but it is much faster. On the LETOR 3.0 dataset it takes about a second to train on any of the folds and datasets. The algorithm for solving the quadratic program is a straightforward extension.
- A basic soft-margin kernel SVM implementation in Python. Support Vector Machines (SVMs) are a family of nice supervised learning algorithms that can train classification and regression models efficiently and with very good performance in practice. SVMs are also rooted in convex optimization and Hilbert space theory, and there is a lot of.
- the training set. To help the algorithm capture nonlinear boundaries, functions of the input variables, such as polynomials, could be added to the set of predictor variables [1]. This extension of the algorithm is called kernel SVM. In contrast, kNN is a nonparametric algorithm because it avoids

- In this post, we will understand the concepts related to SVM (Support Vector Machine) algorithm which is one of the popular machine learning algorithm. SVM algorithm is used for solving classification problems in machine learning. Lets take a 2-dimensional problem space where a point can be classified as one or the other class based on the value of the two dimensions (independent variables.
- The Algorithm::SVM object provides accessor methods for the various SVM parameters. When a value is provided to the method, the object will attempt to set the corresponding SVM parameter. If no value is provided, the current value will be returned. See the constructor documentation for a description of appropriate values
- It is very hard to characterize correctly. First, there are two complexities involved: at training time and at test time. For linear SVMs, at training time you must estimate the vector w and bias b by solving a quadratic problem, and at test time.
- Let's see how we can use a simple binary SVM classifier based on the data above. If you have downloaded the code, here are the steps for building a binary classifier. 1. Prepare data: We read the data from the files points_class_0.txt and points_class_1.txt. These files simply have x and y coordinates of points — one per line
- ed step-size is taken in the opposite direc-tion. We show that with high probability over the choice of the random examples
- 2. Gaussian Kernel. Take a look at how we can use polynomial kernel to implement kernel SVM: from sklearn.svm import SVC svclassifier = SVC (kernel= 'rbf' ) svclassifier.fit (X_train, y_train) To use Gaussian kernel, you have to specify 'rbf' as value for the Kernel parameter of the SVC class
- SVM: We use SVM for the final classification of images. Before I go into details into each of the steps, let's understand what are feature descriptors. (Taken from StackOverflow) A feature descriptor is an algorithm that takes an image and outputs feature descriptors / feature vectors

unking algorithm that uses pro jected conjugate gradien t (PCG) (9). The new SVM learning algorithm is called Se quential Minimal Optimization (or SMO). Unlik e previous SVM learning algorithms, whic h use n umerical quadratic programming (QP) as an inner lo op, SMO uses an analytic QP step. Because SMO sp ends most of its time ev aluating the. Extracting a subset of informative genes from microarray expression data is a critical data preparation step in cancer classification and other biological function analyses. Though many algorithms have been developed, the Support Vector Machine - Recursive Feature Elimination (SVM-RFE) algorithm is one of the best gene feature selection algorithms For this task, we will train three popular classification algorithms - Logistics Regression, Support Vector Classifier and the Naive-Bayes to predict the fake news. After evaluating the performance of all three algorithms, we will conclude which among these three is the best in the task. The Data Set. The dataset used in this article is taken. SVM works relatively well when there is a clear margin of separation between classes. SVM is more effective in high dimensional spaces. SVM is effective in cases where the number of dimensions is greater than the number of samples. SVM is relatively memory efficient; Disadvantages: SVM algorithm is not suitable for large data sets