Contents
Why train accuracy higher than test accuracy?
Test accuracy should not be higher than train since the model is optimized for the latter. Ways in which this behavior might happen: you did not use the same source dataset for test. You should do a proper train/test split in which both of them have the same underlying distribution.
What is meant by training accuracy?
Informally, accuracy is the fraction of predictions our model got right. Formally, accuracy has the following definition: Accuracy = Number of correct predictions Total number of predictions.
What is the difference between ACC and Val_acc?
3 Answers. Now, coming to the log, ‘acc’ refers to accuracy of what was trained against. ‘val_acc’ refers to validation set. Note that val_acc refers to a set of samples that was not shown to the network during training and hence refers to how much your model works in general for cases outside the training set.
Should I use accuracy or F1?
Accuracy is used when the True Positives and True negatives are more important while F1-score is used when the False Negatives and False Positives are crucial. In most real-life classification problems, imbalanced class distribution exists and thus F1-score is a better metric to evaluate our model on.
Why accuracy is not a good measure?
… in the framework of imbalanced data-sets, accuracy is no longer a proper measure, since it does not distinguish between the numbers of correctly classified examples of different classes. Hence, it may lead to erroneous conclusions …
How is confusion matrix related to train accuracy?
Your confusion matrix tells us how much it is overfitting, because your largest class makes up over 90% of the population. Assuming that you test and train set have a similar distribution, any useful model would have to score more than 90% accuracy: A simple 0R-model would. Your model scores just under 80% on the test set.
What’s the accuracy of a Python train model?
Assuming that you test and train set have a similar distribution, any useful model would have to score more than 90% accuracy: A simple 0R-model would. Your model scores just under 80% on the test set.
What should the confusion matrix look like in Python?
Assuming that you test and train set have a similar distribution, any useful model would have to score more than 90% accuracy: A simple 0R-model would. Your model scores just under 80% on the test set. If you would look at the confusion matrix relatively (in percentages) it would look like this:
What does overfitting mean in Python train accuracy?
Overfitting. What I would make up of your results is that your model is overfitting. You can tell that from the large difference in accuracy between the test and train accuracy. Overfitting means that it learned rules specifically for the train set, those rules do not generalize well beyond the train set.