One tool that helps them do this is the Support Vector Machine (SVM) algorithm. Thanks to its versatility and precision, it has found applications in a variety of industries, from finance to medicine, and continues to grow in popularity. Tools such as PS CLEMENTINE PRO facilitate the use of the SVM algorithm. The intuitive interface, advanced analysis features and the ability to visualise results allow users to put SVM models into practice quickly and efficiently. This allows analysts to focus on drawing conclusions and making decisions, rather than wasting time on complex technical processes.
How does the SVM work?
Support Vector Machine is a machine learning algorithm that focuses on data classification and regression. Its goal is to build a model that best separates classes in a data set.
SVM is therefore an algorithm that helps to separate data into groups or predict which group new data belongs to. The operation of this algorithm can be described as the process of finding the best way to divide a dataset so that a new point can be assigned to the appropriate group based on the SVM model previously learned. Its task is to find the optimal boundary (known as a hyperplane) that best separates these groups, while leaving an adequate margin of safety.
Fig.1
Division of the dataset into groups, without transformation
A simplified working of the algorithm is visualised above. In Figure 1 we have the initial situation - the collection consists of two types of observations, coloured green and purple. These two groups can be separated, but not by a simple linear model, but by a curve. New, unknown observations will be assigned to the green or purple group depending on which side of the line they appear.
Fig.2
Partitioning of the dataset into groups, after transformation with the SVM algorithm
As can be seen in Fig.2, the SVM algorithm streamlines this task by separating the classes in the set. The optimal boundary between them, delineated by the grey line in Figure 2, is the aforementioned hyperplane. The points lying closest to the hyperplane are called support vectors, and they play a key role in building the model.
Advantages and disadvantages of the SVM algorithm
Advantages of the SVM algorithm include:
- high performance with small data sets – SVM performs well in situations where relatively few examples are available, making it ideal for analysts working with limited data resources,
- versatility – the algorithm can be applied to both simple and more complex cases where the boundaries between groups of data are irregular. SVM is able to cope with this by using appropriate mathematical methods,
- robustness to overfitting – by focusing on finding the best way to separate the data, SVM avoids overly complex model fitting to the data.
However, like any algorithm, SVM also has its weaknesses. The heavily optimisation-oriented approach tends to be computationally expensive, which, with large datasets, translates into long model training times. The algorithm is also sensitive to outlier cases and anomalies in the data. The analyst must also choose the parameters to build the model appropriately.
Practical applications of SVM
The SVM algorithm is used in many fields, from image classification and speech recognition to medical data analysis and customer segmentation in marketing. SVM can be the basis of models responsible for face recognition, sentiment analysis or spam detection.
In PS CLEMENTINE PRO, analysts can easily apply SVM to different types of tasks. The example shown below is the analysis of cancer samples, where data on the properties of the cells are fed into the tool and the algorithm creates a model to accurately distinguish between sample types. A researcher can create an SVM model that uses values of similar cell characteristics in samples from other patients to make an early assessment of whether a sample is benign or malignant.
Fig.3
Analytical stream building an SVM model to classify cancer samples
Summary
The SVM algorithm is a powerful tool that allows analysts to effectively use data to solve classification and regression problems. Its precision, robustness to overfitting and versatility make it a popular choice in many industries, from medicine to marketing.
Despite some limitations, such as being time-consuming or sensitive to anomalies, SVM remains one of the more commonly chosen algorithms in data analysis. With tools such as PS CLEMENTINE PRO, the use of SVM becomes easy and accessible to a wide range of users, supporting accurate and data-driven decision-making.