Table of Contents
Why are support vector machines a popular machine learning technique?
It is effective in high dimensional spaces. It is effective in cases where the number of dimensions is greater than the number of samples. It uses a subset of training points in the decision function (called support vectors), so it is also memory efficient.
Why is SVM sensitive to outliers?
Despite its popularity, SVM has a serious drawback, that is sensitivity to outliers in training samples. The penalty on misclassification is defined by a convex loss called the hinge loss, and the unboundedness of the convex loss causes the sensitivity to outliers.
What is the use of a Support Vector Machine?
What is SVM? 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.
Which SVM is sensitive to outliers?
The problem with maximal/hard margin classifiers is that they are very sensitive to outliers. They also only work if the data is able to be separated linearly. A way around our problem is to allow for misclassifications this is called soft margin classification.
What can you do to make SVM classifiers less sensitive to the outliers?
To make a threshold that is not so sensitive to the outliers we must allow misclassification. Choosing a threshold that allows misclassifications (we not take in consideration outliers) is an example of Bias/Variance Tradeoff that plagues all of machine learning.
What is logistic regression in machine learning?
Logistic Regression is a Machine Learning algorithm which is used for the classification problems, it is a predictive analysis algorithm and based on the concept of probability. The hypothesis of logistic regression tends it to limit the cost function between 0 and 1 .
What is the difference between regression and classification in machine learning?
Classification is the task of predicting a discrete class label. Regression is the task of predicting a continuous quantity.
What is the meaning of Support Vector Machine?
A support vector machine (SVM) is a type of deep learning algorithm that performs supervised learning for classification or regression of data groups. An SVM builds a learning model that assigns new examples to one group or another. By these functions, SVMs are called a non-probabilistic, binary linear classifier.
Why is it called a Support Vector Machine?
The constraint that needs to be satisfied for a training instance to become a support vector. The solution to our problem, i.e., the optimal (maximum-margin) hyperplane remains unchanged if we remove all training instances but the support vectors. That is why they are given the name ‘support vectors’.