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Home » Applied Sciences » Computer Science » Data Mining (V-U) » Techniques to Improve Classification Accuracy: Ensemble Methods: Random Forest & its advantages (V-U)

Techniques to Improve Classification Accuracy: Ensemble Methods: Random Forest & its advantages (V-U)

Course:Data Mining (V-U)
Discipline:Applied Sciences
Institute:Virtual University
Instructor(s): Dr.Usman Ghani
Level:Undergraduate

Data Mining (V-U)

A multi-dimensional data model: 3D data cube representation (V-U)
A multi-dimensional data model: Data Warehouse Design (V-U)
A multi-dimensional data model: Data warehouse snowflake schema (V-U)
A multi-dimensional data model: Fact Constellation Schema (V-U)
A multi-dimensional data model: Measures of Data Cube (V-U)
A multi-dimensional data model: Typical OLAP Operations (V-U)
Bag of words model (V-U)
Basic Concepts of Classification: A Two-Step Process (V-U)
Basic Statistical Descriptions of Data: Measuring the Central Tendency (V-U)
Basic Concepts of Classification: Introduction (V-U)
Basic Statistical Descriptions of Data: Measuring the Dispersion of Data (V-U)
Basic Concepts of Classification: Supervised vs. Unsupervised Learning (V-U)
Basic Statistical Descriptions of Data: Symmetric vs. Skewed Data (V-U)
Basic Concepts of Cluster Analysis: Challenges of Clutering (V-U)
Bayes Classification Methods: Naïve Bayes Classifier (V-U)
Basic Concepts of Cluster Analysis: Clustering Applications (V-U)
Bayes Classification Methods: Naïve Bayes Classifier: An Example (V-U)
Basic Concepts of Cluster Analysis: Clustering as a Preprocessing Tool (V-U)
Bigram based correction & its example (V-U)
Basic Concepts of Cluster Analysis: Clustering criteria (V-U)

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