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Develop a Model for the Imbalanced Classification of Good and Bad Credit

#artificialintelligence

Misclassification errors on the minority class are more important than other types of prediction errors for some imbalanced classification tasks. One example is the problem of classifying bank customers as to whether they should receive a loan or not. Giving a loan to a bad customer marked as a good customer results in a greater cost to the bank than denying a loan to a good customer marked as a bad customer. This requires careful selection of a performance metric that both promotes minimizing misclassification errors in general, and favors minimizing one type of misclassification error over another. The German credit dataset is a standard imbalanced classification dataset that has this property of differing costs to misclassification errors. Models evaluated on this dataset can be evaluated using the Fbeta-Measure that provides a way of both quantifying model performance generally, and captures the requirement that one type of misclassification error is more costly than another. In this tutorial, you will discover how to develop and evaluate a model for the imbalanced German credit classification dataset. Develop an Imbalanced Classification Model to Predict Good and Bad Credit Photo by AL Nieves, some rights reserved.


Develop a Model for the Imbalanced Classification of Good and Bad Credit

#artificialintelligence

Misclassification errors on the minority class are more important than other types of prediction errors for some imbalanced classification tasks. One example is the problem of classifying bank customers as to whether they should receive a loan or not. Giving a loan to a bad customer marked as a good customer results in a greater cost to the bank than denying a loan to a good customer marked as a bad customer. This requires careful selection of a performance metric that both promotes minimizing misclassification errors in general, and favors minimizing one type of misclassification error over another. The German credit dataset is a standard imbalanced classification dataset that has this property of differing costs to misclassification errors. Models evaluated on this dataset can be evaluated using the Fbeta-Measure that provides a way of both quantifying model performance generally, and captures the requirement that one type of misclassification error is more costly than another. In this tutorial, you will discover how to develop and evaluate a model for the imbalanced German credit classification dataset. Develop an Imbalanced Classification Model to Predict Good and Bad Credit Photo by AL Nieves, some rights reserved.


Develop a Model for the Imbalanced Classification of Good and Bad Credit - AnalyticsWeek

#artificialintelligence

Misclassification errors on the minority class are more important than other types of prediction errors for some imbalanced classification tasks. One example is the problem of classifying bank customers as to whether they should receive a loan or not. Giving a loan to a bad customer marked as a good customer results in a greater cost to the bank than denying a loan to a good customer marked as a bad customer. This requires careful selection of a performance metric that both promotes minimizing misclassification errors in general, and favors minimizing one type of misclassification error over another. The German credit dataset is a standard imbalanced classification dataset that has this property of differing costs to misclassification errors. Models evaluated on this dataset can be evaluated using the Fbeta-Measure that provides a way of both quantifying model performance generally, and captures the requirement that one type of misclassification error is more costly than another. In this tutorial, you will discover how to develop and evaluate a model for the imbalanced German credit classification dataset. Develop an Imbalanced Classification Model to Predict Good and Bad Credit Photo by AL Nieves, some rights reserved.


Caret Package - A Practical Guide to Machine Learning in R

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Caret Package is a comprehensive framework for building machine learning models in R. In this tutorial, I explain nearly all the core features of the caret package and walk you through the step-by-step process of building predictive models. Be it a decision tree or xgboost, caret helps to find the optimal model in the shortest possible time. Caret nicely integrates all the activities associated with the model development in a streamlined workflow, for nearly every major ML algorithm available in R. Actually we will not just stop with the caret package but will also go a step ahead and see how to smartly ensemble predictions from multiple best models and possibly produce an even better prediction using caretEnsemble. Caret is short for Classification And REgression Training. With R having so many implementations of machine learning algorithms, spread across packages it may be challenging to keep track of which algorithm resides in which package. Sometimes the syntax and the way to implement the algorithm differ across packages combined with preprocessing and looking at the help page for the hyperparameters (parameters that define how the algorithm learns) can make building predictive models an involved task. Well, thanks to caret because no matter which package the algorithm resides, caret will remember that for you and may just prompt you to run install.package Later in this tutorial I will show how to see all the available ML algorithms supported by caret (it's a long list!) and what hyperparameters can be tuned.


Predictive Analytics using Machine Learning

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Below you will read in the training and test data which are already split for you to load separately. Then use unnest() from tidytext to create the tidy version with one word per record. Now that you have train and test data loaded and tidied, you can see how many songs exist per artist/author. Since the dataset has songs and book pages, I'll refer to them each as a document. The features that you will create are based on documents and their associated metadata, so it's important to understand this concept.