High leverl summary for multi label, multi class classifier

1 - Data preprocessing and Data wrangling:

• Use pivoting to convert multi-class, multi-label data into multi-label, binary class data. this can be done using MultiLabelBinarizer(it is also a One-hot encoder).

2 - Data split - Use train-split / k-fold crsoss validation / stratified(balanced) shuffle split:

• Have single training/testing dataset.

3 - Use OneVsRestClassifier to solve multi-label data by wrapping these algorithms like LinearSVM or Naive Bayes.

• Tf-Idf vectorizer to convert the string in to numeric value - have appropriate parameters set.
• english stop_words from english Corpus
• Parameter tunning through GridSearchCV

4 - Model Evaluation

• Use precision, recall and f1 score to evaluate the model.
• Accuracy is not suited measure in multilabel scenario