Machine Learning Classification in R using Support Vector Machine (SVM) with IRIS Dataset is a popular technique used in Data Science to classify data into different categories. SVM is a supervised learning algorithm that can be used for both classification and regression tasks. The main idea behind SVM is to find a hyperplane that separates the data into different classes.

The IRIS dataset is a popular dataset used for machine learning classification tasks. It contains 150 observations of iris flowers with four features: sepal length, sepal width, petal length, and petal width. The dataset also contains the species of the iris flower which is the target variable. The three species in the dataset are: Setosa, Versicolor, and Virginica.

To use SVM for classification in R, we first need to load the IRIS dataset. This can be done using the built-in dataset in R called “iris”. Once the dataset is loaded, we need to split the data into training and testing sets. The training set is used to train the model and the testing set is used to evaluate the performance of the model.

Next, we need to fit the SVM model to the training data. The model will learn the relationship between the features and the target variable. We can also specify different parameters such as the kernel type, regularization parameter, and cost parameter. These parameters can affect the performance of the model and can be tuned using techniques such as cross-validation.

Once the model is trained, we can use it to make predictions on the testing data. The predictions can then be compared to the actual values to evaluate the performance of the model. We can use metrics such as accuracy, precision, recall, and F1-score to evaluate the performance of the model.

It’s important to note that SVM is not always the best technique for classification. Depending on the dataset and the problem at hand, other techniques such as Random Forest or Neural Networks may be more suitable. It’s essential to evaluate the performance of different models and choose the one that performs the best.

In conclusion, Support Vector Machine is a powerful technique for classification tasks in Machine Learning. It can be used with the IRIS dataset in R to classify the species of iris flowers. By properly tuning the parameters and evaluating the performance of the model, we can achieve high accuracy in our predictions.

In this Applied Machine Learning & Data Science Recipe (Jupyter Notebook), the reader will find the practical use of applied machine learning and data science in R programming:

Machine Learning Classification in R using Support Vector Machine with IRIS Dataset.

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• Machine Learning Classification in R using Support Vector Machine with IRIS Dataset.
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Machine Learning Classification in R using Support Vector Machine with IRIS Dataset:

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