Lost your password? Please enter your email address. You will receive a link and will create a new password via email.
Please briefly explain why you feel this question should be reported.
Please briefly explain why you feel this answer should be reported.
Please briefly explain why you feel this user should be reported.
Machine Learning
Supervised and unsupervised learning are two primary methods in machine learning. Supervised learning involves training a model on labeled data. This means the model learns to map inputs to desired outputs. For example, predicting house prices based on features like size and location. Common algoritRead more
Supervised and unsupervised learning are two primary methods in machine learning.
Supervised learning involves training a model on labeled data. This means the model learns to map inputs to desired outputs. For example, predicting house prices based on features like size and location. Common algorithms include linear regression, logistic regression, and decision trees.
Unsupervised learning deals with unlabeled data. The goal is to find patterns or structures within the data. Clustering and association rule mining are examples. Common algorithms are k-means clustering for grouping data points and Apriori for discovering relationships between items.
Essentially, supervised learning is like a teacher providing correct answers, while unsupervised learning is like exploring a dataset without guidance.
See lessMachine Learning
Supervised and unsupervised learning are two primary methods in machine learning. Supervised learning involves training a model on labeled data. This means the model learns to map inputs to desired outputs. For example, predicting house prices based on features like size and location. Common algoritRead more
Supervised and unsupervised learning are two primary methods in machine learning.
Supervised learning involves training a model on labeled data. This means the model learns to map inputs to desired outputs. For example, predicting house prices based on features like size and location. Common algorithms include linear regression, logistic regression, and decision trees.
Unsupervised learning deals with unlabeled data. The goal is to find patterns or structures within the data. Clustering and association rule mining are examples. Common algorithms are k-means clustering for grouping data points and Apriori for discovering relationships between items.
Essentially, supervised learning is like a teacher providing correct answers, while unsupervised learning is like exploring a dataset without guidance.
See less