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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ValueError: bad input shape
I have multilabel problem.
I was using onevsrestclassifier and now i want to use onevsoneclassifier.
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Why is the accuracy of a LinearSVC not the same as the SDGClassifier?
I'm fine tuning parameters for a linear support vector machine. There are multiple ways to do it, but I wanted to compare LinearSVC and SDGClassifier in terms of time. I expected the accuracy score to ...
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Support Vector Machines
I have ASCII data which converted from raster data in ArcGIS. I would like to use Support Vector Machine (SVM) algorithm to create a flood prediction map. My question is that can I use ASCII file ...
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Standardization on training and split data
I am confused on which of the following should be used for standardization:
method 1: fit transforming training data and only transforming test data
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GridSearchCV() to fine tune outputs ValueError and FitFailedWarning
I would like to fine tune some parameters for my linear SVM.
This is the code:
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Including validation set in my code for a Linear SVM classifier returns a Type error
I'm using a predict function for a linear SVM classifier:
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How to include validation set in the pipeline to tune parameters for an SVM?
I have a dataset already divided into train, test and validation set. How can I insert the validation in my pipeline?
Code:
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Support Vectors of SVM
I have read somewhere that the value of slack variables of support vectors is not 0. Does that mean the points lying in the wrong region e.g a positive point lying in the negative region will also be ...
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In SVM, what do the points and axis represent?
Usually the axis represent features and points represent the value. But for example in case of document classification, where each document is represented as a feature, what do the axis and points on ...
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When does it make sense to choose gradient descent for SVM over liblinear?
I understand using gradient descent methods with SVM is intractable if you've used the kernel trick. In that case, best to use libsvm as your solver.
But in the case that you are not using a kernel ...
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Encoding for classifiers
I have some doubts regarding encoding (i am not familiar with tasks like these) categorical variables in order to use them as parameters in a model like logistic regression or SVM.
My dataset looks ...
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Variables for SVM [closed]
I would like to predict if an email is spam or not spam based on the information that I have, i.e. date, email address, subject and text.
Three of these parameters are text data, so they would need to ...
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Binary classification: how to transform features in real numbers?
I want to train a binary classification algorithm for spam detection using labeled data set. The dataset has the following features:
...
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How to change voice features in python without affecting speech/language features?
I am trying to build a CNN model which should be able to identify the language being spoken in an audio file. I have extracted the MFCC matrix (for 13 coefficients) for each audio file and trained it....
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Adaptive Sampling Strategies for SVM?
I am an Engineer interested in creating a surrogate model of a certain phenomenon in the context of reliability engineering. Essentially my quantity of interest is the Limit state function/stability ...
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How does C have effects on bias and variance of a Support Vector Machine?
The minimization problem for SVM can be written as-
$$\overset{\text{min}}{\theta} C\sum_{i = 1}^{m}{[y^icost_1(\theta^Tx^i) + (1-y^i)cost_0(\theta^Tx^i)]} + \frac12\sum_{j = 1}^n{\theta_j}^2$$
Now, ...
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Classifiers and accuracy
I would like to ask you how to use classifier and determine accuracy of models.
I have my dataset and I already cleaned the text (remove stopwords, punctuation, removed empty rows,...).
Then I split ...
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NotFittedError says this StandardScaler instance is not fitted yet while using inverse_transform() [closed]
I have a dataset and i have used Support Vector Regression.So i needed to use StandardScaler module from sklearn.preprocessing fro Feature Scaling.
After training my model when i came to predict it ...
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Can we use BERT for only word embedding and then use SVM/RNN to do intent classification?
According to this article, "Systems used for intent classification contain the following two components:
Word embedding, and a classifier." This article also evaluated BERT+SVM and Word2Vec+...
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What is lagrangian?
I'm watching an SVM tutorial.
At 6:38 he mentions lagrangian, which is a term I'm not familiar with.
So I googled it, hoping to find the Wikipedia article about it, ...
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Single image feature reduction at inference time : SVM
I am trying to train a SVM classifier using scikit-learn.. At training time I want to reduce the feature vector dimension.
I have used PCA to reduce the dimension.
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i'm using GridSearchCV to find parameter C for SVC() classifier present in sklearn.svm . I'm not getting the optimal result desired
this is a screenshot of my code. i used abc.best_estimator_ (my GridSearchCV model) to find out best results. As you can see grid has values of C=1 and C=100 along with other values. abc....
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Don't understand why I get an inverse ROC curve for SVM (Python)
I build an SVM classifier but get an inverse ROC curve. The AUC is only 0.08. I've used the same datasets to build a Logistic Regression classifier and a Decision Tree classifier, and the ROC curves ...
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Accuracy gain vs amount of data in Neural Networks
There's a theoretical question I tackled upon in the excellent book Neural Networks and Deep Learning by Michael Nielsen, which I would love to discuss about.
The ...
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Support Vector Machines with soft margin: solving the dual form
I am currently struggling with finding an analytical solution for the $\alpha_k$.
I have derived the following constrained optimization problem:
$$
L = \sum_{i=1}^{N} \alpha_i - \frac{1}{2} \sum_{i=1}^...
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How does SVM classify images?
I have read about SVM and understood that for complex divisions, the SVM theoretically plots the data into a higher dimensional plane such that the in the new dimension the data is linearly separable ...
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Stacked Model performance?
I am currently working with a dataset that seems very easily separable and I have an accuracy of 99% for SVM (NN-98%, RF-98%, DT-96-97% and I have checked for leakage & overfitting). As part of my ...
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How to use unigram and bigram as an feature on SVM or logistic regression [closed]
How to use unigram and bigram as an feature to build an Natural Language Inference model on SVM or logistic regression?on my dataset i have premise, hypotesis and label column. I'm planning to use the ...
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Decision tree and SVM for text classification - theory
I used 4 classifiers for my text data: NB, kNN, DT and SVM. As for NB and kNN I fully understand how they work with text - how we can count probabilities for all words in NB and how to use similarity ...
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Biasing SVM algorithm towards particular subset of data
I'm training an SVM model for sentiment analysis, based on social media data eg. tweets.
The model will be trained using a small selection of a particular company's tweets in order to classify new ...
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Interpretation of scikit-learn one class svm scores
How can I interpret the scores generated by the function score_samples(X) from a scikit-learn OneClassSVM model? Is there a way ...
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Is C = 1/lambda in SVM?
I was looking through the documentation for SVM.SVC where it said:
C: float, default=1.0
Regularization parameter. The strength of the regularization is inversely proportional to C. Must be strictly ...
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ValueError: Found input variables with inconsistent numbers of samples [duplicate]
I am trying to do svm model training and it gives this error:
ValueError: Found input variables with inconsistent numbers of samples: [91, 212]
Code:
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Text Classification on a very small data set with a lot of classes
I have a data set consisting of 455 rows spread over 21 different classes. The data set is imbalanced as well as you can see below.
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What supervised machine learning model can be used to generate a scorecard-like result?
A scorecard is typically used in Credit Application. One very common model for developing a credit scorecard is logistic regression since it has well-defined probabilities.
Apart from logistic ...
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PCA shows overlapping boundaries, then why SVM performs best
I am trying to understand which model might work for a given problem before trying the models, I find this case against my knowledge. Please guide what I am missing. I am new to Data Science.
Here is ...
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Which model is better for incremental learning?
I'm trying to implement face recognition. I'm planning to use some model (like DeepFace) to extract discriminative features and then use a classifier to recognize the faces. I'm confused as to which ...
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Identify the parameter causing the anomaly in a multivariate dataset
I have a payment transaction dataset with a large number of predictor variables. I am trying to build a model for anomaly detection and I have evaluated various algorithms/approaches for the same like ...
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What is the 1 Unit in the contraint of SVM: $y_i(wx_i+b) \geq1$
I am following this note on SVM.
The constraint, $y_i(wx_i+b) \geq 1$, basically said all inputs, $x_i$, lie at least 1 unit away from the hyperplane on the correct side.
What does it mean by 1 unit?...
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Tuning SVM C parameter
I would like to ask for help regarding my model.
I have a dataset of preprocessed images and I performed a binary classification with SVM on Python.
I tuned the value of the c parameter from 0.001 to ...
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AxisError: axis 1 is out of bounds for array of dimension 1
I've used svm classifier. Now I need to construct the confusion matrix. Here is the code that I have used.
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How to improve results in classification problems (SVM, Logistic Regression and MultiNaive Bayes)?
I am new on Machine Learning and building models but a lot of tutorials has given me the chance to learn more about this topic.
I am trying to build a predictive model for detecting fake news.
The ...
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If we use a generalized quadratic loss in a SVM model, what generalization performance bounds can be derived?
If we write an objective function: $$\frac{1}{2}||\vec{w}||^2 + C \sqrt{\vec{\xi}}' S \sqrt{\vec{\xi}}$$ with the usual SVM constraints, and $S_{i,j} = e^{-\gamma || \vec{x_i} - \vec{x_i}||^2}$, where ...
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Hyperparameter tuning one-class svm
I have a problem where I am trying to apply a one-class svm to detect outliers. I am training on a dataset of true cases using a one-class radial svm and then predicting for both false and true cases. ...
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Amount of data needed for deep learning vs support vector machine
I often read about the fact, that the amount of data to train and get a generalizing model for a deep learning algorithm is much higher in comparison, e.g. to a support vector machine. It makes sense, ...
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Suspiciously low False Positive rate with Naive Bayes Classifier?
I am performing phishing URL classification, and I am comparing several ML classifiers on a balanced 2-class data-set (legitimate URL, phishy URL).
The ensemble and boosting classifiers such as ...
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Two-class model with predicted scores needed - classification or regression approach
In my problem, step one is to build a model to classify cases as one of True or False (1 or 0 could also be used obviously). Once the optimum model is found, step two is to retrieve probabilities for ...
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Having trouble understanding the x and y axis in SVM when training and testing data
I wrote some code based on this article.
In the code in the article they have created a partition of 80 percent test and 20 percent data
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How to use a RBF kernel to create a "Kernel Space" using the similarity of each pair of point?
I am trying to use Semi-Unsupervised clustering using reinforcement learning following this paper.
Assume I have n data-points each of which has d dimensions. I also have c pairwise constraints of ...
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Can I use SVC() as a base_estimtor for ensemble methods?
I am currently testing out a few different ensemble methods on my dataset. I've heard that you can also use support vector machines as base learners in boosting and bagging methods but I am not sure ...
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