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forecasting:regression [2020/12/02 00:47] jclaudio [Decision Tree Regression] |
forecasting:regression [2021/09/19 21:59] (current) |
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| ==== Decision Tree Regression ==== | ==== Decision Tree Regression ==== | ||
| - | **Form:** Universal. Can be used with any form. Just need to adjust the SVM Kernel accordingly. | + | **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\\ | **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]]\\ | **Library used:** [[https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeRegressor.html#sklearn.tree.DecisionTreeRegressor|sklearn.tree.DecisionTreeRegressor]]\\ | ||
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| {{ :forecasting:dectreeregoutput.png?400 |}} | {{ :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.// | //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.// | ||