Good morning,
kindly clarify the differences between supervised and unsupervised machine learning
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Good morning @Abhaya_simha,
Supervised models require both data and a target (X, y(X)) at the moment of training, unsupervised does not require a target (X).
For instance, classifiers require a target (maybe if the price of a house is above or below a given threshold) while clustering algorithms do not (maybe they make cluster of similar houses without predicting the price).
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Supervised Machine Learning is the algorithm that know in advance the features (X) and the target ( Y). In the supervised algorithm we alreaddy know which target we are going to predict.
However, Unsupervised Machine Learning is the algorithm that doest not know the target in advance and try to use the structure of feature to obtain the target
I would rather avoid speaking about the target for unsupervised machine learning, there could be many objectives (identify groups, anomalous data points, do a 2D visualization of a high dimensional dataset that preserves some of the original relationships between pairs of data points, extract features for a downstream task…).
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