(9)
4.7 out of 5
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SAS Visual Data Mining and Machine Learning
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SAS Visual Data Mining and Machine Learning Features
What are the features of SAS Visual Data Mining and Machine Learning?
Data Analysis
- Analysis
- Data Interaction
Decision Making
- Modeling
- Data Visualizations
- Report Generation
Top Rated SAS Visual Data Mining and Machine Learning Alternatives
SAS Visual Data Mining and Machine Learning Categories on G2
Filter for Features
Statistical Tool
Scripting | Supports a variety of scripting environments | Not enough data | |
Data Mining | Mines data from databases and prepares data for analysis | Not enough data | |
Algorithms | Applies statistical algorithms to selected data | Not enough data |
Data Analysis
Analysis | Analyzes both structured and unstructured data 10 reviewers of SAS Visual Data Mining and Machine Learning have provided feedback on this feature. | 85% (Based on 10 reviews) | |
Data Interaction | Based on 10 SAS Visual Data Mining and Machine Learning reviews. Interacts with data to prepare it for visualizations and models | 90% (Based on 10 reviews) |
Decision Making
Modeling | Based on 11 SAS Visual Data Mining and Machine Learning reviews. Offers modeling capabilities | 86% (Based on 11 reviews) | |
Data Visualizations | Creates data visualizations or graphs 11 reviewers of SAS Visual Data Mining and Machine Learning have provided feedback on this feature. | 91% (Based on 11 reviews) | |
Report Generation | Generates reports of data performance 11 reviewers of SAS Visual Data Mining and Machine Learning have provided feedback on this feature. | 91% (Based on 11 reviews) | |
Data Unification | Unifies information on a singular platform | Not enough data |
Model Development
Language Support | Supports programming languages such as Java, C, or Python. Supports front-end languages such as HTML, CSS, and JavaScript | Not enough data | |
Drag and Drop | Offers the ability for developers to drag and drop pieces of code or algorithms when building models | Not enough data | |
Pre-Built Algorithms | Provides users with pre-built algorithms for simpler model development | Not enough data | |
Model Training | Supplies large data sets for training individual models | Not enough data | |
Pre-Built Algorithms | Provides users with pre-built algorithms for simpler model development | Not enough data | |
Model Training | Supplies large data sets for training individual models | Not enough data | |
Feature Engineering | Transforms raw data into features that better represent the underlying problem to the predictive models | Not enough data |
Machine/Deep Learning Services
Computer Vision | Offers image recognition services | Not enough data | |
Natural Language Processing | Offers natural language processing services | Not enough data | |
Natural Language Generation | Offers natural language generation services | Not enough data | |
Artificial Neural Networks | Offers artificial neural networks for users | Not enough data | |
Computer Vision | Offers image recognition services | Not enough data | |
Natural Language Understanding | Offers natural language understanding services | Not enough data | |
Natural Language Generation | Offers natural language generation services | Not enough data | |
Deep Learning | Provides deep learning capabilities | Not enough data |
Deployment
Managed Service | Manages the intelligent application for the user, reducing the need of infrastructure | Not enough data | |
Application | Allows users to insert machine learning into operating applications | Not enough data | |
Scalability | Provides easily scaled machine learning applications and infrastructure | Not enough data | |
Managed Service | Manages the intelligent application for the user, reducing the need of infrastructure | Not enough data | |
Application | Allows users to insert machine learning into operating applications | Not enough data | |
Scalability | Provides easily scaled machine learning applications and infrastructure | Not enough data |
System
Data Ingestion & Wrangling | Gives user ability to import a variety of data sources for immediate use | Not enough data | |
Language Support | Supports programming languages such as Java, C, or Python. Supports front-end languages such as HTML, CSS, and JavaScript | Not enough data | |
Drag and Drop | Offers the ability for developers to drag and drop pieces of code or algorithms when building models | Not enough data |
Generative AI
AI Text Generation | Allows users to generate text based on a text prompt. | Not enough data | |
AI Text Summarization | Condenses long documents or text into a brief summary. | Not enough data | |
AI Text Generation | Allows users to generate text based on a text prompt. | Not enough data | |
AI Text Summarization | Condenses long documents or text into a brief summary. | Not enough data | |
AI Text-to-Image | Provides the ability to generate images from a text prompt. | Not enough data |
Agentic AI - Data Science and Machine Learning Platforms
Autonomous Task Execution | Capability to perform complex tasks without constant human input | Not enough data | |
Multi-step Planning | Ability to break down and plan multi-step processes | Not enough data | |
Cross-system Integration | Works across multiple software systems or databases | Not enough data | |
Adaptive Learning | Improves performance based on feedback and experience | Not enough data | |
Natural Language Interaction | Engages in human-like conversation for task delegation | Not enough data | |
Proactive Assistance | Anticipates needs and offers suggestions without prompting | Not enough data | |
Decision Making | Makes informed choices based on available data and objectives | Not enough data |
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