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Global accuracy

Global accuracy

Model accuracy refers to the degree to which a machine learning model correctly predicts the target outcome based on the input data. It is a common classification performance metric used to evaluate the effectiveness of a machine learning model. Model accuracy is typically expressed as a percentage, with 100% accuracy meaning that the model has correctly predicted the target outcome for all examples in the evaluation dataset.

A Sample Model Accuracy Graph

NextBrain will provide you with model accuracy for both training and test set. Keep in mind that model accuracy is only one aspect of model performance and may not always be the most important metric, especially in situations where the consequences of false predictions are severe, such as in medical diagnosis or fraud detection. In such cases, other metrics such as precision, recall, and F1 score may be more relevant. We can see the definition for such metrics as follows:

Precision: Proportion of positive predictions that are actually correct.
Recall (Sensitivity, Hit Rate): Proportion of actual positive cases that are correctly identified by the model.
F1 Score: Harmonic mean of precision and recall.

Meanwhile, you can evaluate the performance of a regression model in machine learning model with the following metrics:

Mean Absolute Error (MAE): The average absolute difference between the predicted and actual values.
Mean Squared Error (MSE): The average squared difference between the predicted and actual values.
Root Mean Squared Error (RMSE): The square root of the mean squared error.
R-Squared (R2): A measure of the proportion of variability in the target variable that can be explained by the predictors.

All of these metrics provide different ways to measure the difference between the predicted values and the actual values. In general, lower values of MAE, MSE, and RMSE indicate a better-performing model, while a higher value of R2 indicates a better fit of the model to the data. It is important to choose an appropriate metric based on the characteristics of the problem and the specific requirements of the application.

Updated on: 02/02/2023

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