Differences

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--- forecasting:regression [2020/10/19 20:26]
jclaudio
+++ forecasting:regression [2021/09/19 21:59] (current)
@@ Line 21: / Line 21: @@
   - Apply the **.predict** method to your regressor to make any predictions about your data
 **Sample code and output:**\\
-{{ :forecasting:simpleregresscode.png |}}
+{{ :forecasting:simpregcode.png?1600 |}}
 {{ :forecasting:simpleregressoutput.png |}}\\
 //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.//
@@ Line 48: / Line 48: @@
 **Library used:** [[https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.PolynomialFeatures.html#sklearn.preprocessing.PolynomialFeatures|sklearn.preprocessing.PolynomialFeatures]]\\
 **General workflow:**
-- Use the **iloc[//rows//, //columns//].values** method from the **pandas** library to grab all columns which correspond to your various independent variables. Note that using iloc[:, :-1].values grabs all rows and all columns except for the last column of your dataset. This assumes that your .csv file was organized such that the dependent variable is in the last column.
+  - Use the **iloc[//rows//, //columns//].values** method from the **pandas** library to grab all columns which correspond to your various independent variables. Note that using iloc[:, :-1].values grabs all rows and all columns except for the last column of your dataset. This assumes that your .csv file was organized such that the dependent variable is in the last column.
   - Repeat the previous step for your dependent variable using y = dataset.iloc.[:, -1].values
   - Import **LinearRegression** class from **sklearn.linear_model**
@@ Line 58: / Line 58: @@
   - Apply the **.predict** method to your regressor to make any predictions about your data
 **Sample Code and Output:**
+{{ :forecasting:polyregcode.png?1600 |}}
+{{ :forecasting:polyregoutput.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.//
 ==== Support Vector Regression ====
@@ Line 64: / Line 67: @@
 **Library used:** [[https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVR.html#sklearn.svm.SVR|sklearn.SVM.svr]]\\
 **General workflow:**
+  - Use the **iloc[//rows//, //columns//].values** method from the **pandas** library to grab all columns which correspond to your various independent variables. Note that using iloc[:, :-1].values grabs all rows and all columns except for the last column of your dataset. This assumes that your .csv file was organized such that the dependent variable is in the last column.
+  - Repeat the previous step for your dependent variable using y = dataset.iloc.[:, -1].values
+  - Import **StandardScaler** class from **sklearn.preprocessing**
+  - Create an 2 instances of the **StandardScaler()** class, one for your independent matrix, and one for your dependent matrix
+  - Apply the **StandardScaler().fit_transform** method to your independent and dependent variables to perform feature scaling accordingly
+  - Import **SVR** class from **sklearn.svm**
+  - Create an instance of the **SVR()** class and set your kernel to whatever you want (Radial Basis Function is commonly used)
+  - 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 |}}
+//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.//