Evaluation metrics, also known as performance measures or evaluative metrics, are quantitative measurements used to evaluate the performance and quality of a model or algorithm in solving a particular problem. It provides a standardized way to evaluate and compare different models and algorithms based on specific criteria. Error Metrics Error Read more…
Evaluation metrics, also known as performance measures or evaluative metrics, are quantitative measurements used to evaluate the performance and quality of a model or algorithm in solving a particular problem. It provides a standardized way to evaluate and compare different models and algorithms based on specific criteria. In our case, Read more…
We have talked about ARIMA and SARIMA models previously, however, we have never shown a real case step by step. Let’s first recap, to make sure we know what an ARIMA model is. ARIMA model ARIMA (Auto-Regressive Integrated Moving Average) is a popular time series forecasting model that combines autoregressive Read more…
Part II: Precision/Recall and Sensitivity/Specificity In the previous article on evaluation metrics for classification problems we explained that accuracy is a very useful metric and very easy to interpret. However, if the dataset we have is not balanced, it will be completely useless. We also defined what a confusion matrix Read more…
Part I: Accuracy and Confusion Matrix There are many different Machine Learning problems, and even within them, there are several ways the objective of the project and the data we have can affect how we assess our model’s performance. We will start with the evaluation metrics used in classification models. Read more…