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Online Machine Learning with Our Support Vector Machine Calculator

Train and Predict with Support Vector Machine: An Online Calculator

Welcome to our free online calculator for Support Vector Machines (SVM). SVM is a powerful algorithm used for binary classification problems, where the goal is to separate data into two classes represented by -1 and 1. SVM is a supervised machine learning technique.

With our Support Vector Machines (SVM) calculator, you can easily train and predict with your own data. Simply upload your dataset and select the kernel type. Our calculator will train the SVM model on your data and provide you with the accuracy, precision, recall, and F1-score of the model. You can also visualize the decision boundary of the model and see how well it separates the two classes.

After training the data, our calculator also provides the ability to make predictions using the trained SVM model. This is one of the unique features that our calculator offers for free. Additionally, the calculator generates informative plots, including a confusion matrix and scatter plot, which displays the position of the two independent variables (in the case of using two independent variables). While our calculator does not currently offer a decision boundary plot that separates the data with two distinct colors, it provides accurate and useful information for your SVM analysis

For additional information on entering data, see the documentation

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Please provide your information below
? Please copy and paste the data from a spreadsheet program such as Excel into this location.
sepal_lengthsepal_widthpetal_lengthpetal_widthspecies
5.43.41.50.4-1
4.42.91.40.2-1
5.13.51.40.2-1
5.52.34.01.31
6.12.84.01.31
5.62.93.61.31
4.52.31.30.3-1
5.54.21.40.2-1
5.03.51.60.6-1
5.43.41.70.2-1
5.53.51.30.2-1
5.13.31.70.5-1
6.33.34.71.61
5.82.64.01.21
5.72.63.51.01
5.73.81.70.3-1
5.12.53.01.11
4.93.11.50.1-1
5.13.81.60.2-1
4.63.11.50.2-1
5.84.01.20.2-1
5.13.41.50.2-1
5.13.51.40.3-1
4.93.11.50.1-1
6.42.94.31.31
5.23.41.40.2-1
5.13.71.50.4-1
5.13.81.90.4-1
4.83.01.40.3-1
6.43.24.51.51
5.03.41.50.2-1
5.72.84.51.31
6.73.05.01.71
4.93.01.40.2-1
6.02.75.11.61
6.82.84.81.41
5.24.11.50.1-1
4.43.01.30.2-1
5.43.91.70.4-1
6.22.24.51.51
4.63.61.00.2-1
5.03.31.40.2-1
4.33.01.10.1-1
4.93.11.50.1-1
5.93.04.21.51
4.92.43.31.01
5.63.04.11.31
6.52.84.61.51
4.63.21.40.2-1
7.03.24.71.41
5.82.74.11.01
5.33.71.50.2-1
5.13.81.50.3-1
5.22.73.91.41
6.03.44.51.61
4.83.01.40.1-1
4.73.21.30.2-1
5.93.24.81.81
4.83.41.60.2-1
5.72.84.11.31
5.03.01.60.2-1
6.93.14.91.51
4.43.21.30.2-1
5.52.64.41.21
5.63.04.51.51
5.03.41.60.4-1
5.73.04.21.21
5.62.53.91.11
6.02.94.51.51
6.63.04.41.41
6.73.14.41.41
6.12.84.71.21
6.32.34.41.31
5.52.43.71.01
6.13.04.61.41
4.83.11.60.2-1
5.52.43.81.11
5.82.73.91.21
6.73.14.71.51
5.43.04.51.51
4.83.41.90.2-1
5.62.74.21.31
5.02.33.31.01
5.23.51.50.2-1
6.32.54.91.51
6.12.94.71.41
5.52.54.01.31
5.03.21.20.2-1
6.22.94.31.31
5.03.61.40.2-1
5.43.91.30.4-1
6.02.24.01.01
5.02.03.51.01
5.74.41.50.4-1
4.73.21.60.2-1
5.72.94.21.31
6.62.94.61.31
5.43.71.50.2-1
5.03.51.30.3-1
4.63.41.40.3-1

Support Vector Machine Result

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Frequently Asked Questions (FAQ) About Support Vector Machine (SVM)

We understand that you may have some questions while using our Support Vector Machines (SVM) calculator. To help you get started, we've put together a list of common questions and answers.

Support Vector Machines (SVM) is a powerful algorithm used for binary classification problems, where the goal is to separate data into two classes represented by -1 and 1. SVM is a supervised machine learning technique.

The kernel is a mathematical function used by the SVM algorithm to transform the data into a higher-dimensional space. This transformation makes it easier to separate the data into two classes.

Our SVM calculator supports three types of kernels: linear, polynomial, and Gaussian.

The accuracy of the SVM model depends on the quality and size of the dataset. Our calculator provides you with the accuracy, precision, recall, and F1-score of the model, so you can evaluate its performance.

Our calculator is designed to work with clean and well-formatted datasets. However, it is recommended that you preprocess your data by scaling the features or applying other transformations that may improve the performance of the model.

Our calculator provides two types of visualizations: the confusion matrix, which shows the number of true positive, true negative, false positive, and false negative predictions made by the model, and a scatter plot that shows the position of the data points in a two-dimensional space (only available if the dataset has one dependent variable and two independent variables).

Yes, our SVM calculator allows you to easily train and predict with your own data. Simply enter your data, select the kernel type, and our calculator will train the SVM model on your data. After training, you can use the same model to predict new values by manually entering the data.