NLP Tutorial 17 – Multi-Label Text Classification for Stack Overflow Tag Prediction
Multi-Label Text Classification in Python with Scikit-Learn.
We will use the “StackSample:10% of Stack Overflow Q&A” dataset. It is a problem statement of a multilabel text classification algorithm. We will be developing a text classification model that analyzes a textual description of questions and predicts multiple labels associated with the question. We will implement a multilabel text classification algorithm for a tag suggestion system using Multi-Label Text Classification in Python which is a subset of multiple output models.
Text preprocessing is performed on the text data and the cleaned data is loaded for text classification. We will be implementing text Vectorization on text data, encode the tag labels using MultilabelBinarizer and model Classical classifiers(SGC classifier, MultiNomial Naive Bayes Classifier, Random Forest Classifier,…) for modeling and compare the results.
In machine learning, Classification is a type of supervised learning. classification refers to a predictive modeling problem where a class label is predicted for a given input sample. It specifies the class to which data point belongs to and is best used when the output has finite and discrete values. There are 4 types of classification tasks that you encounter, they are
1. Binary Classification
2. Multiclass Classification
3. MultiLabel Classification
4. Imbalanced classification
The algorithm’s accuracy can be increased if we use multi-label text classification using BERT or Keras multi-label text classification. You can also use multi-label text classification with XLNET and GPT-2 and GPT-3.
#NaturalLanguageProcessing #MultiLabelClassification #StackOverflowDataset
Watch till last for a detailed explanation.
04:36 Notebook Setup
18:30 Multi-Label Binarizer
21:14 TF-IDF Vectorizer
31:02 SGDClassifier, LogisticRegression, and SVM
42:48 Jaccard Similarity Score
51:16 Test the Model with Real Dataset
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