Data Science – codewindow.in

At first we have to know what is supervised and unsupervised learning? so, Supervised and unsupervised learning are two primary types of machine learning algorithms used to train models to make predictions or identify patterns in data.

Supervised learning is used when the goal is to predict an output variable based on input features, while unsupervised learning is used when the goal is to identify patterns or structure in the data without prior knowledge of the output variables.

The major difference between supervised and unsupervised learning is the presence or absence of labeled data.

Supervised learning, involves training a model on labeled data, where each data point is associated with a known output or response variable. The goal of supervised learning is to learn a mapping between input features and output variables, so that the model can accurately predict the output for new, unseen data.

Unsupervised learning, on the other hand, involves training a model on unlabeled data, where the goal is to identify patterns or structure in the data without prior knowledge of the output variables. Unsupervised learning can be used for tasks such as clustering, where the goal is to group similar data points together, or dimensionality reduction, where the goal is to represent the data in a lower-dimensional space while preserving its key features.

Another key difference is that supervised learning is a form of supervised training, where the model is provided with feedback in the form of labeled data, whereas unsupervised learning is a form of unsupervised training, where the model learns without any feedback.

In summary, the major difference between supervised and unsupervised learning is the presence or absence of labeled data, and whether the model is trained with or without feedback.