This is the second (and last) part of the series dealing with the formal comparison of Machine Learning (ML) algorithms from a statistical point of view. In this post, we examine how statistical tests are applied to performance data of ML algorithms.
Apache Ignite is a distributed in-memory cache, query and processing platform for working with large-scale data sets in real-time (leaving aside, streaming processing, Spark integration, Machine learning grid, Ignite FileSystem, persistence, transactions…)
Have you ever watched the cooking teaching shows? You have probably noticed that chefs have usually already all the ingredients separated and chopped. Likewise, a data scientist will be more useful and creative building models rather than spending time with data preprocessing…
In industry, when a practitioner (often a Data Scientist) uses a machine learning algorithm to build a predictive model to solve a real-world problem, they are interested in the performance when the model is deployed into a production environment…
Spark Streaming is one of the most widely used frameworks for real time processing in the world with Apache Flink, Apache Storm and Kafka Streams. However, when compared to the others, Spark Streaming has more performance problems and its process is through time windows instead of event by event, resulting in delay.
Data analysts are often confronted with a seemingly difficult decision: to choose between a simple model or a more complex one. Discover more in this post in which Carlos del Cacho explains the unexplainable.
With this article, we want to start a series of posts on how to use Stratio Data Centric to solve problems that involve the analysis of large graphs.
We’re always hearing technology companies claim that the future lies in becoming a data-driven organisation. But what about the data-centric approach? Is the journey the same as towards a data-driven approach? Is the result the same?
The last chapter of the three-part series on Evolutionary Feature Selection with Big datasets. We will address some fundamental design aspects of a Genetic Algorithm (GA) and commonly chosen options, to then move on to the CHC algorithm and a distributed approach for Feature Selection.
The key to the success of these methods lies in the rich representations these models build, which are generated after an exhaustive and computationally expensive learning process. Deep learning is starting to become essential to specialized areas of business. Nonetheless, it is not the panacea…
