The first method is to use the mean of the data as an estimator for the missing values. This can be done by using a model with a linear regression, logistic regression, or even a naive Bayes classifier.
Another method is to use the median as an estimator for missing values. This can be done by using linear regression or naive Bayes classifiers.
There are many ways in which unsupervised learning algorithms can be used. One example would be sentiment analysis, where we want to know whether a text has positive or negative sentiment. Another example would be clustering, where we want to identify groups of documents with similar topics or themes.
One of the key components of a machine learning algorithm is the training set. It contains all the information that is needed to train an algorithm and make it learn from experience. The problem with this type of a training set is that it can have missing values, which means that some data is not available for training.