Supervised vs unsupervised learning
Supervised learning and unsupervised learning are two main approaches to machine learning
The main difference between supervised and unsupervised learning is that
supervised learning uses labeled data to learn a mapping from input features to output labels,
while unsupervised learning uses unlabeled data to find patterns and relationships in the data
Supervised learning is a type of machine learning where the algorithm is trained on a labeled dataset,
- meaning that the data points have both input features and corresponding output labels.
- The goal of supervised learning is to learn a mapping from input features to output labels,
- so that the model can make predictions on new, unseen data.
- Examples of supervised learning include regression, classification, and decision trees.
Unsupervised learning, on the other hand, is a type of machine learning
-where the algorithm is trained on an unlabeled dataset,
-meaning that the data points only have input features and no corresponding output labels.
The goal of unsupervised learning is to find patterns or relationships in the data,
without being guided by any specific output labels.
Examples of unsupervised learning include clustering, dimensionality reduction, and anomaly detection.
Supervised learning refers to a type of Machine learning in which the machine requires external supervision for learning from data.
- It contains the models which are trained using the labeled dataset.
- Moreover, it solves problems like regression and classification
Where unsupervised learning refers to a type of machine learning in which
- the machine does not require any external supervision for learning from the data.
- This can be trained using the unlabelled dataset.
- And, it using for solving problems like association and clustering problems.