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--- forecasting:regression [2020/12/01 02:33]
jclaudio
+++ forecasting:regression [2021/09/19 21:59] (current)
@@ Line 76: / Line 76: @@
   - Apply the **SVR().fit** method to your independent variable and your dependent variable
 **Sample Code and Output:**
+{{ :forecasting:suppvectregcode.png?1600 |}}
 {{ :forecasting:suppvectregoutput.png?400 |}}
-==== Decision Tree Regression ====
+//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 ====
+**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 ====
+**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.//