Differences
This shows you the differences between two versions of the page.
Both sides previous revision Previous revision Next revision | Previous revision | ||
forecasting:regression [2020/12/01 02:44] jclaudio [Support Vector Regression] |
forecasting:regression [2021/09/19 21:59] (current) |
||
---|---|---|---|
Line 79: | Line 79: | ||
{{ :forecasting:suppvectregoutput.png?400 |}} | {{ :forecasting:suppvectregoutput.png?400 |}} | ||
//Note that we can easily visualize our results by using a 2D plot. This type of plot is only possible when we have no more than **1** independent variable.// | //Note that we can easily visualize our results by using a 2D plot. This type of plot is only possible when we have no more than **1** independent variable.// | ||
- | ==== Decision Tree Regression ==== | ||
+ | ==== Decision Tree Regression ==== | ||
+ | **Form:** Universal. Can be used with any form.\\ | ||
+ | **When to use it:** Used when you want to divide your dataset into smaller subs-sets in the form of a tree structure\\ | ||
+ | **Library used:** [[https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeRegressor.html#sklearn.tree.DecisionTreeRegressor|sklearn.tree.DecisionTreeRegressor]]\\ | ||
+ | **General workflow:** | ||
+ | - Import **DecisionTreeRegressor** class from **sklearn.tree** | ||
+ | - Create an instance of the **DecisionTreeRegressor()** class | ||
+ | - Apply the **.fit** method to your independent and dependent variables | ||
+ | - Apply the **.predict** method to your regressor to make any predictions about your data | ||
+ | **Sample Code and Output:** | ||
+ | {{ :forecasting:dectreeregcode.png?1600 |}} | ||
+ | {{ :forecasting:dectreeregoutput.png?400 |}} | ||
+ | //Note that we can easily visualize our results by using a 2D plot. This type of plot is only possible when we have no more than **1** independent variable.// | ||
==== Random Forest Regression ==== | ==== Random Forest Regression ==== | ||
+ | **Form:** Universal. Can be used with any form.\\ | ||
+ | **When to use it:** Used when you want to have multiple random decision trees to improve your regression results.\\ | ||
+ | **Library used:** [[https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestRegressor.html#sklearn.ensemble.RandomForestRegressor|sklearn.ensemble.RandomForestRegressor]]\\ | ||
+ | **General workflow:** | ||
+ | - Import **RandomForestRegressor** class from **sklearn.ensemble** | ||
+ | - Create an instance of the **RandomForestRegressor()** class | ||
+ | - Apply the **.fit** method to your independent and dependent variables | ||
+ | - Apply the **.predict** method to your regressor to make any predictions about your data | ||
+ | **Sample Code and Output:** | ||
+ | {{ :forecasting:randforestregcode.png?1600 |}} | ||
+ | {{ :forecasting:randforestregoutput.png?400 |}} | ||
+ | //Note that we can easily visualize our results by using a 2D plot. This type of plot is only possible when we have no more than **1** independent variable.// |