Explain the difference between supervised and unsupervised machine learning algorithms, and provide examples of each.

2 Answers
Answered by suresh

Supervised vs Unsupervised Machine Learning Algorithms

Supervised vs Unsupervised Machine Learning Algorithms

Supervised Machine Learning: Supervised machine learning algorithms learn from labeled training data, where the input data is paired with the correct output. The algorithm's goal is to learn a mapping function to map the input to the output.

Example of Supervised Machine Learning: Support Vector Machines (SVM) and Decision Trees.

Unsupervised Machine Learning: Unsupervised machine learning algorithms work with unlabeled data, where the input data is not paired with the correct output. The algorithm's goal is to find patterns and relationships in the data without guidance.

Example of Unsupervised Machine Learning: K-means Clustering and Principal Component Analysis (PCA).

Answered by suresh

Supervised vs. Unsupervised Machine Learning Algorithms

Supervised vs. Unsupervised Machine Learning Algorithms

In the field of data science, supervised and unsupervised machine learning algorithms are two fundamental approaches used for training models to make predictions and uncover patterns in data.

Supervised Machine Learning:

Supervised machine learning algorithms are trained on labeled data, where the input features are paired with corresponding output labels. These algorithms learn to map input features to the correct output labels by using the labeled data for training.

Examples of supervised machine learning algorithms include:

  • Linear Regression
  • Support Vector Machines (SVM)
  • Decision Trees
  • Random Forest

Unsupervised Machine Learning:

Unsupervised machine learning algorithms, on the other hand, are trained on unlabeled data, where the algorithm learns to find patterns and relationships within the data without the need for explicit output labels.

Examples of unsupervised machine learning algorithms include:

  • K-Means Clustering
  • Hierarchical Clustering
  • Principal Component Analysis (PCA)
  • Association Rule Learning

Understanding the difference between supervised and unsupervised machine learning algorithms is crucial for selecting the appropriate approach for a given data science problem.

Answer for Question: Explain the difference between supervised and unsupervised machine learning algorithms, and provide examples of each.