November 2014
Beginner to intermediate
446 pages
12h 16m
English
Content preview from Predictive Analytics and Data Mining
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Comparison of Data Mining Algorithms
Classification: Predicting a Categorical Target Variable
| Algorithm | Description | Model | Input | Output | Pros | Cons | Use Cases |
| Decision Trees | Partitions the data into smaller subsets where each subset contains (mostly) responses of one class (either “yes” or “no”) | A set of rules to partition a data set based on the values of the different predictors. | No restrictions on variable type for predictors. | The label cannot be numeric. It must be categorical. | Intuitive to explain to nontechnical business users. Normalizing predictors is not necessary. | Tends to overfit the data. Small changes in input data can yield substantially different trees. Selecting the right parameters can be challenging. | Marketing segmentation, fraud detection. |
| Rule ... |
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