Which algorithm is primarily used for text classification tasks?AprioriSupport Vector Machines (SVM)Hierarchical ClusteringK-MeansClear selection

The Support Vector Machines (SVM) algorithm is primarily used for text classification tasks. Here's a step-by-step explanation:

1. Text Data Collection: The first step in any machine learning task is to collect the data. In this case, the data would be a collection of text documents.

2. Text Preprocessing: This step involves cleaning the text data and converting it into a format that can be fed into the SVM algorithm. This may involve removing stop words (common words like 'is', 'the', 'and', etc.), stemming (reducing words to their root form), and vectorization (converting text data into numerical data).

3. Training the SVM Model: Once the data is preprocessed, it is split into training and testing sets. The training set is used to train the SVM model. This involves feeding the data into the SVM algorithm, which creates a model that can classify the text documents.

4. Testing the SVM Model: The testing set is used to evaluate the performance of the SVM model. The model is used to classify the text documents in the testing set, and its predictions are compared with the actual classifications to determine its accuracy.

5. Model Optimization: If the performance of the SVM model is not satisfactory, its parameters can be tuned and optimized to improve its accuracy.

6. Final Model: Once the SVM model is optimized and its performance is satisfactory, it can be used for text classification tasks.