End-to-End Machine Learning: accuracy metric in R

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End-to-End Machine Learning: accuracy metric in R

When training a machine learning model, it’s important to evaluate its performance to understand how well it will work on new, unseen data. One common way to evaluate the performance of a model is by using a metric called “accuracy.”

Accuracy is a measure of how often the model correctly predicts the outcome of a certain event. It is calculated by dividing the number of correct predictions by the total number of predictions. An accuracy of 100% means that the model has made no mistakes, while an accuracy of 0% means that the model has not made any correct predictions.

In R, there are several ways to calculate accuracy, and several libraries such as caret, mlr, etc. which provide functions to calculate accuracy. Some of the most popular functions are confusionMatrix(), metric() and accuracy() that can be used to calculate accuracy.

It’s important to note that accuracy is not always a reliable metric for evaluating a model’s performance. For example, if there’s a skewed class distribution in the dataset (i.e. one class is overrepresented), then a model that simply predicts the majority class will have a high accuracy but it’s not a good model.

Therefore, it’s always important to use accuracy in conjunction with other performance metrics such as precision, recall, F1-score, etc. and to analyze the confusion matrix to understand how well the model is doing in classifying the different classes.

Overall, accuracy is a simple and widely used metric for evaluating the performance of a machine learning model. However, it’s important to keep in mind that it’s not always reliable and should be used in conjunction with other performance metrics to get a complete picture of a model’s performance.


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End-to-End Machine Learning: accuracy metric in R

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