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Below is a list of the most common customer questions. If you can’t find an answer to your question, please don’t hesitate to reach out to us.

Which algorithm is best for text classification?

Linear Support Vector Machine is widely regarded as one of the best text classification algorithms.

Which algorithm is best for multi class classification?

Most of the machine learning you can think of are capable to handle multi class classification problems, for e.g., Random Forest, Decision Trees, Naive Bayes, SVM, Neural Nets and so on.

Can SVM be used for multi class classification?

Yes. While the other answers are right in that, SVM (as originally proposed in Vapid's paper) is inherently binary classifier and that often multi class Sums are implemented in one vs all fashion. So, it would be wise to not suggest that SVM cannot handle more than two classes.

How do you do the multi class classification?

Load dataset from source. Split the dataset into training and test data. Train Decision tree, SVM, and CNN classifiers on the training data. Use the above classifiers to predict labels for the test data. Measure accuracy and visualize classification.

What is multi class classification problem?

In machine learning, multi class or multinomial classification is the problem of classifying instances into one of three or more classes. (Classifying instances into one of two classes is called binary classification.)

How can I improve my text classification?

Broke the documents in list of words. Removed stop words, punctuations. Performed stemming. Replaced numerical values with '#sum#' to reduce vocabulary size. Transformed the documents into TF-IDF vectors.

Which model is best for text classification?

Linear Support Vector Machine is widely regarded as one of the best text classification algorithms. We achieve a higher accuracy score of 79% which is 5% improvement over Naive Bayes.

How do you text a classification?

Text classification is the process of assigning tags or categories to text according to its content. It's one of the fundamental tasks in Natural Language Processing (NLP) with broad applications such as sentiment analysis, topic labeling, spam detection, and intent detection.

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