SVM stands for support vector machine algorithm. The primary weapon of choice for classification tasks. Alongside the classifier module, SVM features a regressor module. But the classification concepts are the fundamentals of the SVM paradigm. Based on the number of dimensions of a data set, the SVM classifier can separate the two classes of entities by a line, plane or hyperplane. The classification is done by locating the plane or the line exactly in the middle point of two near extremities of the classes. These extreme values lying on the extremity of margins are known as support vectors. The margins are the classifier plane thus depends on these support vectors.
SVM algorithm steps
The deployment of the SVM algorithm is divided into several steps. These steps are like other data science processes, standardized and tested for ultimate performance. The SVM algorithm steps are as follows.
- Loading and importing libraries
- Importing dataset and extracting X and Y variables.
- Preparation of train and test datasets
- Initializing and deploying SVM classifier
- Making predictions
- Checking model performance
Essential components of SVM algorithm
The distance between the extreme values and the classifier boundary is called margin and the SVM algorithm extends the margin to its maximum. And the classifier boundary is then called the maximum margin classifier.
- Hard margin
Hard margins are extremely rigid depending on the scenario. In the case of training sets, the rigid absolution is essential thus is extensively used. But in real-life using hard margins for SVM is simply absurd due to the candid nature of real-world events. In these cases, soft margins are being deployed due to their relaxed approach towards noise in a dataset.
- Soft margin
In the case of most real-life scenarios, the data sets are full of noise and many values tend to cross into the margins. Soft margins are thus utilized as slightly relaxed and noisy data sets. Most real-life cases are candid in nature and might present many instances of noise. Thus in these cases, soft margins are used.
Depending on the dimensionality of a database the classifier boundary can be a line, a plane or a hyperplane. The classifier boundaries are middle lines drawn by the margins supported by support vectors. The classifier boundaries, able to grant maximum margin length, are known as maximum margin classifiers.
Application of SVM algorithm
The use of SVM in real life is extensive. Being the best choice for classification problems, SVM can be deployed in multiple scenarios in real life.
Remote homology detection
The SVM algorithm is also used to detect homology in distantly related proteins and genes. In order to execute such detections, training with large data sets are essential. But after the model is ready, the executions are seamless.
In handwriting recognition
Handwriting recognition is a multi-stage operation done using the SVM algorithm. The structural and statistical features of one’s handwriting are considered, alongside the extracted features. Spontaneous recognition, however, is still not possible as the SVM enabled tool must be trained with sample handwritings in order to perform at its optimum.
Text and hypertext categorization
Text and texts containing added links or other information can be characterized based on the dimensions like space they occupy, presence of external links and color and character of the text.
The most prominent example of image classification by SVM is perhaps histological diagnosis. Histological diagnosis is done by computer vision coupled with SVM. this benediction of SVM is saving countless lives. By adding desirable dimensions SVM can classify any kind of image set and yield relevant results.
In bioinformatics the use of SVM is unparalleled. SVM is used in histology extensively. And in the case of molecular biology, SVM is being deployed in the study of protein dynamics and gene expressions.
Advantages of SVM
- SVM is memory efficient
- SVM works better with clear margins and a distinct classifier boundary between two classes.
- SVM is additionally effective in the case of high dimensional spaces.
- SVM is effective when the number of dimensions exceeds the number of samples.
Disadvantages of SVM
- SVM fails to attend to large data sets.
- Overlapping classes and noise in data sets render the deployment of SVM extremely difficult.
- In cases where the number of features for the samples exceed the number of training data samples, SVM might underperform.
- SVM leaves no room for probabilistic interpretations. As the algorithm classifies the data set in two classes rather qualitatively, based on a line or plane.