machine learning semi-supervised learning it shouldn' surprise you to learn that while we' like all our data to be labeled so we can use the more powerful supervised machine learning techniquesin reality we often start with only minimally labeled dataif it' labeled at all we can use our unsupervised machine learning techniques to analyze what we have and perhaps add labels to the data setbut it will be prohibitively costly to label it all our goal then is to train our predictor models with as little labeled data as possible this is where semi-supervised learning techniques come in--hybrids of the two approaches we've already seen take for example the plot in figure in this casethe data has only two labeled observationsnormally this is too few to make valid predictions does not buy buys figure this plot has only two labeled observations--too few for supervised observationsbut enough to start with an unsupervised or semi-supervised approach common semi-supervised learning technique is label propagation in this techniqueyou start with labeled data set and give the same label to similar data points this is similar to running clustering algorithm over the data set and labeling each cluster based on the labels they contain if we were to apply this approach to the data set in figure we might end up with something like figure one special approach to semi-supervised learning worth mentioning here is active learning in active learning the program points out the observations it wants to see labeled for its next round of learning based on some criteria you have specified for exampleyou might set it to try and label the observations the algorithm is least certain aboutor you might use multiple models to make prediction and select the points where the models disagree the most