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Is there anyone who can help clarify my doubt?
Hello Lily, please find the answer below.
H2O can be used for both classification and regression tasks. When performing classification with H2O, the model predicts a probability distribution for each class. This means it estimates the likelihood of an input belonging to each class. To evaluate how well the classification model is performing, we can use metrics that are typically used for regression tasks.
Regression metrics are usually used to measure the accuracy of a regression model, which predicts a continuous numerical value. However, H2O can also calculate these regression metrics to evaluate the performance of a classification model.
For example, metrics like mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE) can be used to assess the quality of the predicted probabilities in classification. These metrics quantify the difference between the predicted probabilities and the true probabilities for each class.
In addition to regression metrics, H2O also provides classification-specific metrics such as AUC-ROC, log loss, and confusion matrix. These metrics are specifically designed to evaluate the performance of a model in predicting the correct class labels.
Therefore, when evaluating the performance of a classification model with H2O, both regression metrics and classification-specific metrics should be considered based on the specific evaluation goals and requirements.
To summarize, while the metrics for classification and regression in H2O are not exactly the same, regression metrics can be used in classification tasks to assess the accuracy of the predicted probabilities.