# Questions tagged [svm]

Support Vector Machines (SVM) are a popular supervised machine learning algorithm that can be used for classification or regression.

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12 views

### What is meant by data dependent kernel?

I was reading this research paper Isolation kernel and it's effect on SVM wherein they mention in the paper that data dependent kernels depend directly on the data.Is there a simple explanantion that ...
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### Estimating a rbf kernel SVM, followed by Stochastic Gradient Descent

I wanna estimate a rbf SVM to predict property prices. My data set has 11 features and roughly 57,000 rows. When I set C=10, R^2 is about 0.88 while MSE and RMSE are 0.1191 and 0.3451. The results are ...
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### SVMs: where comes the 1 and -1 in hyperplane equations and where is b?

Concering Support Vector Machines (SVM): it is always mentioned that $\textbf{w}^T\textbf{x}_i - b >= 1$, for $\textbf{x}_i$ of class 1 (i.e. $y=1$) and $\textbf{w}^T\textbf{x}_i - b <= -1$, ...
27 views

### Does linear kernel make SVM a linear model?

I have deleloped several SVR models for my case study using the linear kernel, and those models were optimized using the RMSE as criterion. Now Im searching for additional evaluation metrics and it ...
9 views

### How to avoid eegbci.load_data() and analyze each file and change classification methods?

I have a pipeline for EEG analysis using CSP and LDA for classification of EEG signals using MNE. ...
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### Same training and test data is fed to SVM RBF kernel in python and matlab giving different results

I have used 60 % data as training data and 40% data as test data. Exactly same instances of data are fed to SVM RBF kernel in Python and SVM Gaussian in MatLab. But the results of prediction in MatLab ...
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### Is it efficient to use kernel trick in primal form of SVM?

I know we can use Kernel trick in the primal form of SVM. So the hypothesis will be - and optimization objective - We can optimize the above equation using gradient descent, but in this equation ...
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### what is the effect of initial weights on model training for different algorithms

If I do a model training on a dataset with three different algorithms Logistic Regression (L1), SVM(S1) and a Neural Net(N1). If I train the models again with the same data set and same parameters ...
31 views

### regression with noisy target vairable

How can I approach a regression problem where the input data is not noisy but the target variable is noisy? Are there any regression algorithms that are robust to a noisy target variable? Also, is it ...
38 views

### Should bag of words in training set include test set data when doing text classification?

I'm doing text classification to identify 'attacks' from Wikipedia comments using a simple bag of words model and a linear SVM classifier. Because of class imbalance, I'm using the F1 score as my ...
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### Inconsistancy in Sklearn SVM predict() and predict_proba()

Actually I have two questions. One of them is related the bug of sklearn SVM model and the other one is about ROC-AUC score. My first question is related to ROC-AUC score but also includes a bug ...
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### How the Support Vector Machine will perform if the bias b = 0 in the equation of hyperplane?

We have a soft margin linear SVM and the equation is as follows : How the SVM will perform if b = 0, means the hyperplane is passing through the origin ?
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### Non IID variables and SVM Classifier

I am training an SVM model to predict the trend of stock prices (one-day ahead predictions. Classification task). It Had completely slipped from my mind that SVMs assume IID data until I had a ...
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### Linear Learning Machines

I was reading about Linear Learning Machines (LLMs) and learned that it is closely related with SVMs. Would like to know an example of any concrete problems that can be classified by LLM as I couldn't ...
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### What if Training and testing dataset comes from the same source?

I am working on a classification problem in which I have to distinguish between healthy and damaged plates. when I use the combination of k-means clustering and SVM algorithm together with 10-fold ...
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### Get the prediction probability using prediction function

I'm new to SVM models. I took custom SVM classifier from the github. In there standard predict function was overwritten by custom predict function. ...
21 views

### Different learning curves on each run

Sorry for the wrong terminology I might use, since I’m a noob. For my supervised learning project for the university I have a dataset (features and labels) which has to evaluated in several ways and ...
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### How do i improve my accuracy in LinearSVC? Looking for better approaches/advices

I'm struggled to get accuracy around 70 used all the tricks and tips to improve it but couldn't make it my goal is to get at least 90+ accuracy. Trained 2 folders with 4000 images 2000 images for each ...
31 views

### Interpreting one-class SVM

I am new to SVM (one-class) and was practically investigating it. Got some weird result that I can not explain. Let me demonstrate by some small reproducible code and visualization: ...
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### Is it acceptable to use label encoding for nominal categorical data when one hot encoding would create too many features?

I'm working on a short data science project to compare the accuracy of different classification methods. The groups decided to use and compare Random Forest, Naive Bayes and SVM. The dataset we are ...
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### Support Vector Classifier and cost C

Applying SVC (Support Vector Classifier) to the 1-d data shown here: What will be the support vectors for the parameter cost C=0 and C=Infinity? As far as I read about SVM and hyper parameter C I ...
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### Problem in working out an example of SVM: mathematical steps

I am trying to code SVM from scratch using a small toy problem that involves five support vector values. In the code below, there are 5 support vectors arbitrary chosen and denoted by the variables <...
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### Why is the optimal C chosen by GridSearchCV so small?

I'm trying to use GridSearchCV to select the optimal C value in this simple SVM problem with non-separable samples. The issue I'm having is that when I run the code the optimal C is chosen to be ...
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### Need help understanding Hard SVM quadratic program equation

This is from the textbook "Understanding Machine Learning" by Shalev-Schwarz p. 169. Can anyone help me understand why the solutions to this optimization problem need to be divided by the ...
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### Steps to fit a Machine learning model for prediction of up and down market movement

I have around 5 years of data of an index containing many features on a daily basis. I want to classify whether the index will move up or down the next trading day (up or down movement is determined ...
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### Classification with disjoint boxes

Are there any classification schemes like a support vector machine that use axis-aligned boxes instead of hyperplanes? I have a dataset consisting of about a billion points in 9 dimensions, which are ...
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### Finding the dual to an optimization problem on an unsupervised dataset [closed]

We consider the unsupervised dataset $x_1,..x_N \in R^d$ and the optimization problem: $$min_w \,\frac{1}{2}{\left\lVert w \right\rVert}^2,$$ subject to constraints:\forall_{i=1}^N: \phi(x_i)^Tw\...
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### Results of quadratic SVM in Matlab are different from the results obtained in Python

I am trying to replicate a quadratic SVM classifier from Matlab to Python, however I am having different results regarding the accuracy. In Matlab the accuracy is 0.8955 meanwhile in Python the ...
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### How important is outcome variable scaling in SVM regression?

Should I scale outcome variable for SVM regression? What is the magnitude of impact of outcome variable scaling in SVM regression?
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### How to decrease $R^2$ value and change it to positive value [closed]

I'm working on a data, and use regression , as you see bellow: from sklearn.svm import SVR regressor = SVR(kernel = 'linear') regressor.fit(trainX,trainY) above ...
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### Machine learning model (neural network or SVM) for unequal feature matrices size

I have feature matrices obtained from visual bags of words model for various dictionary sizes. Example, Nx5, Nx10, …., Nx15000. Where N is the number of samples and 5, 10, …15000 are the visual ...
159 views

### Preprocessing: StandardScaler() Do we really need mean to be zero?

For instance, many elements used in the objective function of a learning algorithm (such as the RBF kernel of Support Vector Machines or the l1 and l2 regularizers of linear models) assume that all ...
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### Confusion matrix : Train data accuracy lower than test data accuracy and 95%CI meaning

Let's say that I have training data and test data. I trained the model with the training data and then constructed a confusion matrix of the predictions of both. I am getting that the prediction of ...
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### Bert and SVM classification

I'm trying to understand the concepts in the title and how they fit into the task of binary classification. According to my understanding so far, you can encode text using various feature-extraction ...
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### Understanding Learning Curves

I would like to clarify my understanding of learning curves with two example plots below. I am experimenting with small data sets here between 500 and 1500 samples to clarify my understanding. My ...
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### One-Class classification

I am currently using an one class classification svm and I am trying to boost the classification results by employing more than one svm-occ with varying gamma parameters and combine these decisions ...
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### Why does using a standard scalar on my tf idf matrix make it perform better?

I have a TF-IDF matrix transformed on a list of tweets from a data set I am using. I have a pipeline where I initiate a StandardScalar and then next have my SVM with a linear kernel and auto gamma as ...
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### Intuition behind One Class SVM (Scholkopf)

I am trying to understand the intuition behind the idea of finding a hyperplane that separates the training data from the origin in the feature space. Why separation from origin with a hyperplane ...
52 views

### Overfitting in imbalanced dataset

I am working on a dataset related to an insurance company and the objective is to predict if the insurance buyer will claim their travel insurance or not. Training data: https://raw.githubusercontent....
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### SVM is margin determined by nearest datapoint or nearest datapoints?

I am studying support vector machines and different resources seem to define the margin differently. Some define the margin as 2 times the distance to the nearest datapoint. Others define the margin ...
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### How plot GridSearch results?

I trained an SVM model with GridSearch ...
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### UserWarning: No contour levels were found within the data range

I am running the exact example give in this SVM example of Scikit learn without any modification. I get the following warning. ...
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### How to find the initial hyperplane in a Support Vector Machine (SVM)?

As far as I understand Support Vector machines, we are trying to find the optimal hyperplane, out of all hyperplanes that are equidistant from the support vectors. There are an infinite number of ...