SVM Insights | Sumble

What is SVM?

Support Vector Machines (SVMs) are a powerful and versatile set of supervised machine learning algorithms used for classification, regression, and outlier detection. SVMs aim to find the optimal hyperplane that maximizes the margin between different classes in the feature space. They are effective in high-dimensional spaces and are relatively memory efficient.

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What other technologies are related to SVM?

SVM Competitor Technologies

Naive Bayes

Naive Bayes is a classification algorithm that, similar to SVM, can be used for classification tasks. It provides an alternative approach based on probabilistic modeling.

mentioned alongside SVM in 80% (859) of relevant job posts

k-NN

k-Nearest Neighbors is a classification algorithm that is a competitor to SVM as it also performs classification, but uses a different method.

mentioned alongside SVM in 90% (751) of relevant job posts

Decision Forests

Decision Forests (or Random Forests) are ensemble learning methods for classification and regression that are a competitor to SVM. They are a collection of decision trees.

mentioned alongside SVM in 96% (624) of relevant job posts

Random Forest

Random Forest is an ensemble learning method for classification and regression that is a competitor to SVM. It is a collection of decision trees.

mentioned alongside SVM in 27% (1.3k) of relevant job posts

KNN

KNN (k-Nearest Neighbors) is a classification algorithm that is a competitor to SVM as it also performs classification, but uses a different method.

mentioned alongside SVM in 53% (496) of relevant job posts

decision trees

Decision trees are a classification method, and are thus a competitor to SVM.

mentioned alongside SVM in 15% (766) of relevant job posts

GBM

Gradient Boosting Machines are ensemble learning methods that can be used for classification and regression, offering an alternative approach to SVM.

mentioned alongside SVM in 33% (292) of relevant job posts

neural networks

Neural networks are a broad class of machine learning models that can be used for classification and regression, providing a general alternative to SVM.

mentioned alongside SVM in 10% (816) of relevant job posts

SVM Complementary Technologies

PCA

Principal Component Analysis is a dimensionality reduction technique which can be used to pre-process the data before applying SVM, thereby improving performance.

mentioned alongside SVM in 12% (351) of relevant job posts