Model Metrics


r_subheading-Course Description-r_end In this tutorial on r_link-Introduction to Text Analytics with R- https://online.datasciencedojo.com/course/Text-Analytics-with-R-r_end, we will discuss different metrics to evaluate and improve the accuracy of the model. r_break r_break r_subheading-What You'll Learn-r_end • The importance of metrics beyond accuracy for building effective models. r_break • Coverage of sensitivity and specificity and their importance for building effective binary classification models. r_break • The importance of feature engineering for building the most effective models. r_break • How to identify if an engineered feature is likely to be effective in Production. r_break • Improving our model with an engineered feature.

Text Analytics tutorial slides can be accessed r_link-here- https://code.datasciencedojo.com/datasciencedojo/tutorials/tree/master/Introduction%20to%20Text%20Analytics%20with%20R-r_end r_break r_break Download R r_link-here- https://cran.r-project.org/-r_end r_break r_break SMS Spam Collection Dataset used in this tutorial can be accessed r_link-here- https://www.kaggle.com/uciml/sms-spam-collection-dataset-r_end

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