How to evaluate my classification model results by?
Log loss function penalizes wrongly classified cases and also lacks of confidence in correctly-classified cases. Same as ROC-AUC, it does not only consider the classification accuracy or results but also considers the probability assigned to each of the cases from the model. The better the model, the lower the log loss value.
How to calculate confidence intervals for classification models?
A package to calculate confidence intervals for classification positive rate, precision, NPV, and recall using a labeled sample of the population via exact & approximate Frequentist & Bayesian setups. Precision and recall are important metrics for evaluating classification model performance; however, they are only calculable from labeled data.
When to use accuracy in a classification model?
Accuracy is a good measure of how the overall model performs. However, it is not telling you the performance in each category and thus you may miss important information if you purely look at accuracy. It is best to use when a number of cases in different categories are similar or used together with other metrics.
How to reshape data for a classification model?
Reshape your data either using X.reshape (-1, 1) if your data has a single feature or X.reshape (1, -1) if it contains a single sample. DeprecationWarning)
How to get the predicted probabilities of a classification?
For example, if you have your Naive Bayes classifier and you want to obtain probabilities but not classification itself, you could do (I used same nomenclatures as in your code): Hope this helps. Thanks for contributing an answer to Stack Overflow!
How to get prediction scores from exported classification learner SVM?
Error in trainClassifier (line 48) predictors = inputTable (:, predictorNames); About: ‘This struct is a trained model exported from Regression Learner R2018b.’