November 2018
Beginner to intermediate
568 pages
15h 59m
English
Content preview from Data Science, 2nd Edition
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Comparison of Data Science 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 ... |
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