Which of the following represents the percentage of the correct predictions from the total predictions?
dataset
feature
accuracy
class label
accuracy
The percentage of correct predictions from the total predictions is represented by the term "accuracy".
The percentage of correct predictions from the total predictions is represented by the term "accuracy." Accuracy is a measure used to evaluate the performance of a predictive model or classifier. It quantifies the ratio of correct predictions to the total number of predictions made.
To calculate accuracy, you need the dataset, which is a collection of labeled instances used to train and test a predictive model. The dataset contains a set of features, which are the variables or attributes that describe each instance. These features can be numeric, categorical, or binary.
Additionally, the dataset includes a class label, which is the target variable or the ground truth value that the model aims to predict. The class label is used as the reference value to compare against the model's predictions.
To calculate accuracy, you compare the predicted class labels generated by the model against the true class labels in the dataset. You count the number of correct predictions and divide it by the total number of predictions (correct + incorrect), then multiply the result by 100 to express it as a percentage. The accuracy value will range from 0 to 100, with higher values indicating better performance.