Big data is often a buzzword. In recent years the big data word has preoccupied business owners and made department managers drool. One crucial prerequisite to big data is to utilize professional data visualization tools. These tools provide a better way to access, explore and communicate your data. Here are the ten best data visualization tools you should know for 2020. Data visualization software provides a visual representation of a company's data in the form of interactive charts and graphs.
Business users and analysts commonly use spreadsheets and 2D plots to analyze and understand their data. Online Analytical Processing (OLAP) provides these users with added flexibility in pivoting data around different attributes and drilling up and down the multidimensional cube of aggregations. Machine learning researchers, however, have concentrated on hypothesis spaces that are foreign to most users: hyperplanes (Perceptrons), neural networks, Bayesian networks, decision trees, nearest neighbors, etc. In this paper we advocate the use of decision table classifiers that are easy for line-of-business users to understand. We describe several variants of algorithms for learning decision tables, compare their performance, and describe a visualization mechanism that we have implemented in MineSet. The performance of decision tables is comparable to other known algorithms, such as C4.5/C5.0,
Big Data is, as the adjective suggests, is quite large. Businesses are asked to leverage all sorts of data and data types to make decisions and insights on where to move forward. In years past, making sense of Big Data was the purview of engineers and statisticians who spent the better part of a decade studying these patterns and numbers. But, in recent years, software developers have attempted to cut the middle man out. Data analytics is now becoming more accessible to those even if they don't have a doctorate in computer programming.
McGregor, Sean (Oregon State University) | Buckingham, Hailey (Oregon State University) | Houtman, Rachel (Oregon State University) | Montgomery, Claire (Oregon State University) | Metoyer, Ronald (Oregon State University ) | Dietterich, Thomas G. (Oregon State University)
Markov Decision Process (MDP) simulators and optimization algorithms integrate several systems and functions that are collectively subject to failures of specification, implementation, integration, and optimization. We present a domain agnostic visual analytic design and implementation for testing and debugging MDPs: MDPvis.
Hi friends,I was struggling for Decision tree visualization in python.Sometimes there is error due to pydot and sometimes due to graphviz....even though I have installed both in my windows machine but still no luck... please let me know if you know any easy method for this visualization in ipython notebook