#> $ sub_issue 'Debt is not yours', 'Information belongs… #> $ issue 'Attempts to collect debt not owed', 'Inc… #> $ sub_product 'I do not know', 'Credit reporting', 'I d… #> $ product 'Debt collection', 'Credit reporting, cre… C.4 Lasso regularized classification model.C.2 Split into test/train and create resampling folds.B.3 Consumer Financial Protection Bureau (CFPB) complaints.B.2 Opinions of the Supreme Court of the United States.B.1 Hans Christian Andersen fairy tales.10.5 Case study: explainability with LIME.9.6 Case study: training a regression model.9.2 Compare to a recurrent neural network.9 Long short-term memory (LSTM) networks.8.5 Cross-validation for deep learning models.8.2.2 One-hot sequence embedding of text.7.10 What evaluation metrics are appropriate?.7.7 Case study: including non-text data.7.3 Compare to a lasso classification model.7.1.1 Building our first classification model.6.8 What evaluation metrics are appropriate?.6.1.1 Building our first regression model.What processes are in place to handle appeals or mistakes?.What is the accuracy of a simple rule-based alternative?.What are the error rates for sub-groups?.5.6 Using word embeddings in the real world.5.2 Understand word embeddings by finding them yourself.5.1 Motivating embeddings for sparse, high-dimensional data.

3.5 Stop words in languages other than English.

3.4 What happens when you remove stop words.

3.3 All stop word lists are context-specific.

2.5 Tokenization for non-Latin alphabets.

2.4.1 Tokenize to characters, only keeping letters.

2.3 Where does tokenization break down?.2.2.4 Lines, sentence, and paragraph tokens.1.2 A glimpse into one area: morphology.Welcome to Supervised Machine Learning for Text Analysis in R.Supervised Machine Learning for Text Analysis in R.

2.2.3 testout lab