September 2016
Beginner to intermediate
264 pages
9h 26m
English
Content preview from Real-World Machine Learning
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Appendix. Popular machine-learning algorithms
|
Name |
Type |
Use |
Linear/nonlinear |
Requires normalization |
|---|---|---|---|---|
| Linear regression | Regression | Model a scalar target with one or more quantitative features. Although regression computes a linear combination, features can be transformed by nonlinear functions if relationships are known or can be guessed. R: www.inside-r.org/r-doc/stats/lm Python: http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html#sklearn.linear_model.LinearRegression | Linear | Yes |
| Logistic regression | Classification | Categorize observations based on quantitative features; predict target class or probabilities of target classes. R: www.statmethods.net/advstats/glm.html Python: http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html ... |
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