Model Evaluation Archives

MachineX: performance metrics for Model Evaluation

May 5, 2020May 6, 2020Analytics, ML, AI and Data Engineering, Tech Blogsdata science, Evaluation Metrics, Machine Learning, MachineX, Model Evaluation

Reading Time: 6 minutes In this blog, we are going to see how to choose the right metrics for model evaluation in different kinds of applications. There are different metric categories based on the ML model/application, and we are going to cover the popular metrics used in the following problems: Classification Metrics (accuracy, precision, recall, F1-score, ROC, AUC) Regression Metrics (MSE, MAE) there are more metrics like Computer Vision Continue Reading

MachineX: Evaluation Metrics for Classification Models

December 2, 2019December 5, 2019AI, ML, AI and Data Engineering, Studio-ScalaClassification Metrics, Evaluation Metrics, Machine Learning, Model Evaluation

Reading Time: 5 minutes In our last blog post, we have looked at various evaluation metrics for the regression model. Continuing on this we will take a look at the evaluation metrics used for classification models. Classification is about predicting class labels given input data. In binary classification, there are two possible output classes whereas in Multi-class classification we have more than two possible output classes. We are going Continue Reading

MachineX: Evaluation Metrics for a Regression ML Model

June 27, 2019June 27, 2019ML, AI and Data EngineeringEvaluation Metrics, Machine Learning, Model Evaluation, Regression Metrics

Reading Time: 3 minutes In this blog post, we will quickly look at the various metrics to evaluate our regression models. But first, let us briefly discuss one of the best-known model evaluation approach we use which is Train-Test or also known as Train-Validation split. Train-Test Split: In this approach, we split the data into two parts known as Training set and Test set. The model is then trained Continue Reading