What are the evaluation metrics?
Evaluation metrics are used to measure the quality of the statistical or machine learning model. Evaluating machine learning models or algorithms is essential for any project. There are many different types of evaluation metrics available to test a model.
What are the different evaluation metrics?
Evaluation metrics are used to measure the quality of the statistical or machine learning model. There are many different types of evaluation metrics available to test a model. These include classification accuracy, logarithmic loss, confusion matrix, and others.
When to use precision as an evaluation metric?
Precision is a valid choice of evaluation metric when we want to be very sure of our prediction. For example: If we are building a system to predict if we should decrease the credit limit on a particular account, we want to be very sure about our prediction or it may result in customer dissatisfaction.
How are evaluation metrics used for classification problems?
For classification problems, metrics involve comparing the expected class label to the predicted class label or interpreting the predicted probabilities for the class labels for the problem. Selecting a model, and even the data preparation methods together are a search problem that is guided by the evaluation metric.
When to use recall as an evaluation metric?
Recall is a valid choice of evaluation metric when we want to capture as many positives as possible. For example: If we are building a system to predict if a person has cancer or not, we want to capture the disease even if we are not very sure. Recall is 1 if we predict 1 for all examples.
How can I calculate RMSE using cross Val predict?
If this gives estimate of Y (y prediction) for every Y (true Y), why can’t I calculate metrics such as RMSE or coefficient of determination using these results? It seems to be based on how samples are grouped and predicted. From the user guide linked in the cross_val_predict docs: