What is the accuracy of a majority class baseline?

What is the accuracy of a majority class baseline?

An accuracy of 76% can be achieved simply by assigning all instances to majority class.

What is a majority class classifier?

The majority class is simply that having the greatest frequency in the class distribution of training examples reaching the leaf. The set of such rules, one for each path, is the induced classifier and can be used to classify unseen examples. Many different trees may adequately fit a training set.

What is the classification accuracy?

Classification accuracy is simply the rate of correct classifications, either for an independent test set, or using some variation of the cross-validation idea.

What is the baseline prediction?

A baseline prediction algorithm provides a set of predictions that you can evaluate as you would any predictions for your problem, such as classification accuracy or RMSE. The scores from these algorithms provide the required point of comparison when evaluating all other machine learning algorithms on your problem.

How is the majority classifier used in classification?

Given a set of training data, the majority classifier always outputs the class that is in the majority in the training set, regardless of the input. You expect the majority classifier to achieve about 50% classification accuracy, but to your surprise, it scores zero every time.

How is the accuracy of a classifier determined?

The further you get from 50/50, the more accuracy misleads. Consider a dataset with a 99:1 split of negatives to positives. Simply guessing the majority class yields a 99% accurate classifier! In the real world, imbalanced domains are the rule, not the exception!

Why does the majority classifier score zero every time?

You expect the majority classifier to achieve about 50% classification accuracy, but to your surprise, it scores zero every time. Why? My only solution about it is that the training data is inverse to the real data.

Is it possible to get 90 percent classification accuracy?

Achieving 90 percent classification accuracy, or even 99 percent classification accuracy, may be trivial on an imbalanced classification problem.