I'm trying to complete a text classification task with word2vec, the steps I took are:
- preprocess the text in my dataset;
- split the dataset into training set(70%) and test set(30%);
- train wrod2vec model with the text in the training set;
- transfer all text(both in training and test set) into word2vec embeddings;
- approach Naive Bayes, Logistic regression, and random forest to do the classification. RandomizedSearchCV was used to search for the optimal parameters.
- use learning curves(use the data from the training set) to detect if the classifiers overfit or not.
The accuracy of all classifiers is similar, approx. 73%. However, all the learning curves of the classifiers showed that they were overfitted.
Below I give the learning curves:
Could anyone explain the situation? If there are some solutions for my problems?
Thanks in advance!