Continuous features can be measured on a scale and can be divided into smaller parts

The likelihood is expressed by the probability density function for continuous features

Print characteristics like the probability density function, the priors, and some of the datapoints to better understand the dataset

The accuracy of a classification model is the number of correct predictions divided by the total number of predictions. This statistic is used to describe the model's performance.

To determine accuracy, it is necessary to split the dataset into train and test data. The ratio has an impact on how well-trained the model is an how biased the accuracy prediction is. Common train to test ratios include 8:2, 7:3, and 6:4

After the classifier is trained on the training data, it can predict the classification of the test data. The accuracy_score method returns the accuracy of the trained model