Conclusion – Introduction to Text Analytics with R Part 12

In this conclusion to Text Analytics with R we cover topics such as:

– Optimizing our model for the best generalization on new/unseen data.
– Discussion of the sensitivity/specificity trade-off of our optimized model.
– Potential next steps regarding feature engineering and algorithm selection for additional gains in effectiveness.
– For those that are interested, a collection of resources for further study to broaden and deepen their text analytics skills.

Kaggle Dataset:
Kaggle Spam Data Set

The data and R code here

Full Series:
Introduction to Text Analytics with R

More Data Science Material:
[Video Series] Beginning R Programming
[Video Series] Creating a Kaggle Model using R
[Blog] Natural Language Processing with R Programming Books


About The Author
- Data Science Dojo is a paradigm shift in data science learning. We enable all professionals (and students) to extract actionable insights from data.


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