Understanding Supervised vs. Unsupervised Learning
In the realm of Data Science, supervised and unsupervised learning are two fundamental approaches to machine learning. Here's a brief explanation of the key differences:
Supervised Learning
Supervised learning involves training a model on a labeled dataset, where each data point is associated with a corresponding target label. The goal is for the model to learn the mapping between the input features and the target labels so that it can make predictions on new, unseen data.
Examples of supervised learning algorithms include:
- Linear Regression
- Support Vector Machines (SVM)
- Decision Trees
Unsupervised Learning
In contrast, unsupervised learning deals with unlabeled data, where the model is tasked with finding patterns or structures within the data without explicit target labels. The primary objective is to explore the inherent structure of the data and uncover hidden relationships.
Examples of unsupervised learning algorithms include:
- K-means Clustering
- Principal Component Analysis (PCA)
- Association Rule Mining
By understanding the distinction between supervised and unsupervised learning, data scientists can choose the most suitable approach based on the nature of the data and the problem at hand.
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