Model Evaluation

Model evaluation is the process of using multiple statistics and metrics to analyze the performance of a trained model, highlighting both its strengths and weaknesses. Studio provides various methods to evaluate the classification and regression models, such confusion matrix, window visualization, evaluation using Grad-CAM, R-squared (Coefficient of Determination), Mean Squared Error (MSE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) and graphical plots tools such as Quantile - Quantile and Histogram of residuals.

Additionally, you can use Graph UX to evaluate the model performance. Graph UX supports real-time model evaluation functionality which helps in analyzing and monitoring the model predictions before deploying a model to production. It also ensures that the model generates accurate predictions on real-time data.