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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Sklearn SVM slower than when run in GridSearchCV
Problem:
Running SVM in GridSearchCV is faster than running without it and supplying only 1 value of C and no CV.
The AUC on the test set is lower when SVM is run outside of GridSearchCV.
Background:...
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working principle of Support Vector Machine
I have a dataset consisting of numerical features and categorical features. I want train the training set using SVM. SVM is a quadratic optimization algorithm. I would like to know the how SVM works ...
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Feature selection and model performance
Featuretools provides an automated way to generate features from your data, by providing relationships within your data and applying their so-called deep feature synthesis. It generates features like ...
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memory bound for kernel tricks in machine learning
Based on Andrew Ng's lecture on Kernel,
you use training samples (referred as landmarks l) and use them during prediction to construct the higher dimensional representation of the given sample.
This ...
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Classification task in Adaface
I have a closed dataset with 15-20 people (10 images per person) and I use Adaface to extract feature embeddings. I was wondering what is the best classifier? Is SVM (linear Kernel) a good one? What ...
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My model is predicting values for only two labels (instead of 9) how to fix it?
My goal is to predict the count (variable y) based on various features (variable x). My y is most of the time (98.4%) equal to 0, so this data is inflated by 0.
Based on this premise, I thought that ...
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How to solve soft margin SVM using dual lagrangian problem?
I don't know how to use dual lagrangian problem to solve soft margin SVM. Can anyone help me to solve this example with steps calculations:
X_1=(0,2) | y_1=+1
X_2=(0,1) | y_2=-1
X_3=(0,0) | y_3=+1
...
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Medical Image Classifier
from a series of medical images I obtain several descriptors that characterize each of the images.
From these descriptors I want to classify them into normal / pathological and then into normal / each ...
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Precision and AUROC for which class values
I am a newbie in reading research paper and implementing it by myself. I went through the paper Breast Cancer Survival Prediction from Imbalanced Dataset with Machine Learning Algorithms.
Can anyone ...
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SVM's support vectors decision function representataion
I am currently using SVM for my project with 'rbf' kernel.
What i understand from the theory is that the decision function value ...
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How to increase the accuracy of 1 class
Hai I am working with blood transfusion data set using SVM classifier.I applied SVC with C=17 and kernel rbf. It is highly imbalanced data set and I balanced it using SMOTE. But class 1 is performing ...
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Will removing one support vector affect others?
Supppose we have SVM trained on a dataset and the support vectors are $SV=\{x_1,x_2,\cdots,x_n\}$. Then, we know that the decision plan is decided by $SV$. My question is that if we remove one support ...
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Fluctuating accuracy for Naive Bayes Classifier and SVM
I am comparing the classification accuracy between Naive Bayes (NBC), SVM and a Neural Network. I am using a Dataset of ~18K and 26 Labels.
In the current state the Neural Network get always an ...
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What's the input shape for an SVM classifier?
I have a dataset with tensors (there are 12 classes) of shape (700,2000) - height is 700 and width is 2000. I would like to try to use an SVM classifier (just to ...
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Combining a BoW vector with other features: effectivity and feature value ranges
I am working with a Support Vector Machine to predict class prevalence in a binary classification problem.
The model will take a sparse representation of an instance as input, where the number of the ...
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What are some methods to reduce a dataframe so I can pass it as one sample to an SVM?
I need to classify participants in an NLP study into 3 classes, based on multiple sentences spoken by the participant. I performed a feature extraction on each sentence, and so I am left with a matrix ...
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Why would we add regularization loss to the gradient itself in an SVM?
I'm doing CS 231n on my own. I'm looking at this solution to a question that implements a SVM.
Relevant code:
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SVN understanding decision boundaries when using large vs. small values for gamma
It's said that when using a large value for gamma in SVN, the model will fit the training data better than using a small value thus may lead to overfitting. However, looking into the following ...
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Strong bias from Linear SVR meta model
I have built nine meta models based on the model stacking principle, which I compare to a reference model for a number of time series. See the results below. The 22 base models that are trained on 70% ...
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Extracting negative class support vectors (on imbalanced class) and remove them from dataset based on performance
I'm classifying imbalanced dataset using SVM, and I'm trying to prune the support vectors to solve the imbalanced class problem (binary classification). The ideas are, I'm going to first train the ...
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Why would a Linear SVR model greatly outperform a Linear Regression model on model stacking
I have built nine meta models based on the model stacking principle, which I compare to a reference model for a number of time series. See the results below. The 22 base models that are trained on 70% ...
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Should I train multiple models or single model with respective data for multiple parameters?
In one of my projects I have 7 output classes. However, while this makes up a baseline set of results, I also want to test the impact of some experimental parameters.
To avoid any information ...
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Can depth be used as a feature when predicting rock type from well log data?
I am trying to predict the lithofacies, i.e. the rock type, from well log data, a project very similar to the one described in this tutorial.
A well log can be seen as a 1D curve tracking how a given ...
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Get negative predicted value in Support Vector Regresion (SVR)
I am doing Covid-19 cases prediction using SVR, and getting negative values, while there should be no number of Covid-9 cases negative. Feature input that I was used is mobility factor (where have ...
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random_state on train_test_split() appears to have large effect in performance metrics?
To summarize the problem: I have a data set with ~1450 samples, 19 features and a binary outcome where classes are fairly balanced (0.51 to 0.49).
I split the data into a train set and a test set ...
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Training data in sentiment analysis
I'm doing sentiment analysis of tweets related to recent acquisition of Twitter by Elon Musk. I have a corpus of 10 000 tweets and I'd like to use machine learning methods using models like SVM and ...
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Objective Functions in Twin Support Vector Machines
I'm reading paper Twin Support Vector Machines for Pattern Classification by Jayadeva et al. (2007). In that paper, the authors proposed using two non-parallel hyperplanes for classifying two classes. ...
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How to get all the parameters of scikit-learn multiclass SVM classifier?
I have trained my multiclass SVM model for MNIST classification in Python using scikit-learn using the following code:
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Can you use gplearn library to improve an SVM model?
I want to know your thoughts on this.
Someone on the internet recommended this process to me in order to improve the accuracy of my SVM model:
Split dataset with 5 folds stratified k-fold (SKF)
Apply ...
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What is custom SVM kernel?
What is custom kernel in the Support Vector Machine. How is it different from Polynomial kernel. How to implement a custom kernel. Can you provide a code to implement a custom kernel.
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I have a question about scaling test data in svm machine learning (binary)
I am making a machine learning model from qEEG(Quantification) data.
EEG Data form different values for different human characteristics.
To give an example, I will use the breast cancel data of the ...
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How do I use wavelet transform for feature extraction correctly?
I'm trying to classify words based on EMG signals using a support vector machine as my model. My dataset includes 15 classes (words) with 230 repetitions and 1000 features each. I already merged all ...
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Why my models have a pretty high accuracy with a small training dataset?
I was wondering why my models (decision tree, svm, random forest) behave like that, with "high" accuracy on a small training dataset. Is it a sign of overfitting?
The graph represents the ...
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Is SVM a good choice for this imputing a numerical variable?
Let's say I have 10,000 training points, 100,000,000 points to impute, and 5-10 prediction variables/parameters, all numeric (for now). The target variable is numeric, skewed normal with outliers. I ...
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Functional form for machine learning models
I am new to the field of machine learning and I have a question.
Is there a way to print the function of any machine learning model, just like Y=mX + C (equation for straight line). For eg. support ...
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How to improve the accuracy of support vector machine algorithms in machine learning?
I am working with a machine learning project named "Diabetes prediction using support vector machine". In this project I have used Pima Indians Diabetes Database. Using SVM I have got 78% ...
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How to estimate a best fitting line that separates variables on a scatterplot by some third binary outcome variable with 95% accuracy?
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I've been thinking about this and haven't found a non-brute force method of doing it. I have a series of scatterplots depicting the relationship between two predictor variables and a third binary ...
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How to classify a dataset containing variable size list of lists?
I have a dataset which has a list of lists as an input (each row) and the labels are in order of (0-9). The inside lists are of two lengths, 8 and 10. Each input list is of variable length ...
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How to print the corresponding c of the lowest classification error on the validation data
I'm currently measuring the overall classification error for an SVM classifier and I'm varying the regularization value C.
In the following code, how can I print in ...
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Distinguishing text with opposite meanings in SVM (False Information Detection)
I am currently working on a Binary Text Classification Model (False Information Detection) using Support Vector Machine and used TF-IDF as text vectorizer in Python. I have already tried training the ...
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A support vector machine for separating pluses from minus finds a support vector at point (1,0) and a minus support vector at x2=(0,1)
Suppose a support vector machine for separating pluses from minus finds a support vector at point (1,0) and a minus support vector at x2=(0,1). Determine the values of w and b.
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Using SVM as final layer in Convolutional Neural Network
I am working on the implementation of a hybrid CNN-SVM, where I define the use of SVM in the last layer of CNN as shown in this code:
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If an SVM decision boundary is the perpendicular bisector of the line connecting the support vectors, why iterate for it using a loss function?
Would it not make more sense to do some linear algebra to find the vector of the decision boundary? Is that more computationally expensive?
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Input shape of dataset in hybrid CNN-SVM classifier
I am working on hybrid CNN-SVM for classification task, where I aim to use CNN for feature extraction and SVM for classification.
So after the training of my CNN model as below:
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Why the constraint always holds in soft margin SVM?
In the soft margin SVM, the loss minimization function is given as -
Subject to $y_i(w^Tx_i + b) \geq 1 - \varepsilon_i$ and $\varepsilon_i \geq 0$
The 2nd constraint will always be true for any ...
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Why do we take +1. -1 for support vector hyperplane in SVM?
In the SVM, we have 3 hyperplanes, one for separating positive class and negative class, and the other two lying on the support vectors. In the figure -
The equation of hyperplanes lying on support ...
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The best way to work with hybrid CNN-SVM
I am working on a hybrid CNN-SVM where I aim to use CNN for feature extraction and SVM for classification. However, I am confused as after reading related works, I found many approaches:
-Some use SVM ...
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Imbalanced data set with Sample weighting - How to interpret the performance metrics?
Consider a binary classification scenario whereby the True class (5%) is severely outbalanced to the False class (95%). My data set contains numeric data. I am using SKLearn and trying some different ...
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Support Vector Machines || find hyperplane and the w & b and the alpha value
here three points are given, they are the support vectors, two of them belongs to negative class an one is positive class, here we need to find the hyper plane and values of w and b and the alpha ...
