Introduction to Text Analytics with R – Part 9: Model Metrics
This data science tutorial introduces the viewer to the exciting world of text analytics with R programming. As exemplified by the popularity of blogging and social media, textual data if far from dead – it is increasing exponentially! Not surprisingly, knowledge of text analytics is a critical skill for data scientists if this wealth of information is to be harvested and incorporated into data products. This data science training provides introductory coverage of the following tools and techniques:
– Tokenization, stemming, and n-grams
– The bag-of-words and vector space models
– Feature engineering for textual data (e.g. cosine similarity between documents)
– Feature extraction using singular value decomposition (SVD)
– Training classification models using textual data
– Evaluating accuracy of the trained classification models
Part 9 of this video series includes specific coverage of:
– The importance of metrics beyond accuracy for building effective models.
– Coverage of sensitivity and specificity and their importance for building effective binary classification models.
– The importance of feature engineering for building the most effective models.
– How to identify if an engineered feature is likely to be effective in Production.
– Improving our model with an engineered feature.
The data and R code used in this series is available via the public