Remove Data mining Remove Deep Learning Remove Metrics Remove Optimization
article thumbnail

Meta-Learning For Better Machine Learning

Rocket-Powered Data Science

An example of a cold start problem is k -Means Clustering, where the number of clusters k in the data set is not known in advance, and the locations of those clusters in feature space ( i.e., the cluster means) are not known either. What is missing in the above discussion is the deeper set of unknowns in the learning process.

article thumbnail

Data Scientist’s Dilemma – The Cold Start Problem

Rocket-Powered Data Science

If we cannot know that ( i.e., because it truly is unsupervised learning), then we would like to know at least that our final model is optimal (in some way) in explaining the data. In those intermediate steps it serves as an evaluation (or validation) metric. This challenge is known as the cold-start problem !

Insiders

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

article thumbnail

Data science vs. machine learning: What’s the difference?

IBM Big Data Hub

Other challenges include communicating results to non-technical stakeholders, ensuring data security, enabling efficient collaboration between data scientists and data engineers, and determining appropriate key performance indicator (KPI) metrics. Python is the most common programming language used in machine learning.

article thumbnail

Leveraging user-generated social media content with text-mining examples

IBM Big Data Hub

One of the best ways to take advantage of social media data is to implement text-mining programs that streamline the process. What is text mining? Validation and iteration It’s essential to make sure your mining results are accurate and reliable, so in the penultimate stage, you should validate the results.

article thumbnail

Explaining black-box models using attribute importance, PDPs, and LIME

Domino Data Lab

The interest in interpretation of machine learning has been rapidly accelerating in the last decade. This can be attributed to the popularity that machine learning algorithms, and more specifically deep learning, has been gaining in various domains. Methods for explaining Deep Learning.

Modeling 139
article thumbnail

MLOps and the evolution of data science

IBM Big Data Hub

Machine learning (ML), a subset of artificial intelligence (AI), is an important piece of data-driven innovation. Machine learning engineers take massive datasets and use statistical methods to create algorithms that are trained to find patterns and uncover key insights in data mining projects.