What are some of the most common algorithms used in machine learning?
Machine learning, a subset of artificial intelligence, involves the use of algorithms and statistical models to enable computers to perform tasks without explicit instructions by relying on patterns and inference instead. Within this domain, numerous algorithms have been developed to address various types of problems, ranging from classification and regression to clustering and dimensionality reduction.
Does an unsupervised model need training although it has no labelled data?
An unsupervised model in machine learning does not require labeled data for training as it aims to find patterns and relationships within the data without predefined labels. Although unsupervised learning does not involve the use of labeled data, the model still needs to undergo a training process to learn the underlying structure of the data
How do we evaluate the performance of clustering algorithms in the absence of labeled data?
In the field of Artificial Intelligence, specifically in Machine Learning with Python, evaluating the performance of clustering algorithms in the absence of labeled data is a important task. Clustering algorithms are unsupervised learning techniques that aim to group similar data points together based on their inherent patterns and similarities. While the absence of labeled data
What is the difference between k-means and mean shift clustering algorithms?
The k-means and mean shift clustering algorithms are both widely used in the field of machine learning for clustering tasks. While they share the goal of grouping data points into clusters, they differ in their approaches and characteristics. K-means is a centroid-based clustering algorithm that aims to partition the data into k distinct clusters. It
- Published in Artificial Intelligence, EITC/AI/MLP Machine Learning with Python, Clustering, k-means and mean shift, K means with titanic dataset, Examination review
What is the limitation of the k-means algorithm when clustering differently sized groups?
The k-means algorithm is a widely used clustering algorithm in machine learning, particularly in unsupervised learning tasks. It aims to partition a dataset into k distinct clusters based on the similarity of data points. However, the k-means algorithm has certain limitations when it comes to clustering differently sized groups. In this answer, we will consider