Classification and Regression with the BigML Dashboard
7.14 Takeaways
This chapter explains evaluations in detail. Here is a list of key points:
An evaluation allows you to measure your model, ensemble, logistic regression, deepnet, and fusion performance.
In BigML you can perform two types of evaluations: single evaluations and cross-validation evaluations.
You need a model and a testing dataset to create a single evaluation. (See Figure 7.115 .)
You just need an existing dataset to create a cross-validation evaluation. (See Figure 7.116 .)
BigML provides you a range of configuration options before creating your evaluation.
Performance measures are different for classification and regression models.
The confusion matrix is a key element to evaluate the performance of classification models.
You can compare your evaluations measures against models using the mean, the mode, and a random value to predict.
BigML provides different visualizations for the ROC curve, the Precision-Recall curve, the Gain curve, and the Lift curve along with their AUC, K-S statistic and other metrics.
You can compare two or more evaluations built with different configurations and algorithms to select the model with the best performance.
You can download your confusion matrix in Excel format.
You can create and use evaluations via the BigML API and bindings.
You can add descriptive information to your evaluations.
You can move your evaluations between projects.
You can share your evaluations with other people using the secret link.
You can stop your evaluations creation by deleting them.
You can permanently delete an existing evaluation.
![\includegraphics[width=6cm]{images/evaluations/evaluations-workflow}](images/img-0764.png)
![\includegraphics[width=5cm]{images/evaluations/cross-val-workflow}](images/img-0765.png)