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1 | 1 | # 5. Regressor |
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5 | | -<figure><img src="../.gitbook/assets/image (2).png" alt="" width="209"><figcaption></figcaption></figure> |
| 3 | +<figure><img src="../.gitbook/assets/image (329).png" alt="" width="525"><figcaption></figcaption></figure> |
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7 | 5 | 1. Click on the _**Regressor**_ in the _**Machine Learning**_ category. |
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11 | | -<figure><img src="../.gitbook/assets/image (1) (2).png" alt="" width="563"><figcaption></figcaption></figure> |
| 7 | +<figure><img src="../.gitbook/assets/image (330).png" alt="" width="563"><figcaption></figcaption></figure> |
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13 | 9 | 2. _**Model Type**_: Choose the regression model. |
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33 | 29 |
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34 | 30 | ### Linear Regression |
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38 | | -<figure><img src="../.gitbook/assets/image (2) (2).png" alt="" width="563"><figcaption></figcaption></figure> |
| 32 | +<figure><img src="../.gitbook/assets/image (331).png" alt="" width="563"><figcaption></figcaption></figure> |
39 | 33 |
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40 | 34 | 1. _**Fit Intercept**_: Choose whether to include the intercept. |
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45 | 39 |
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46 | 40 | ### Ridge / Lasso |
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50 | | -<figure><img src="../.gitbook/assets/image (3).png" alt="" width="563"><figcaption></figcaption></figure> |
| 42 | +<figure><img src="../.gitbook/assets/image (332).png" alt="" width="563"><figcaption></figcaption></figure> |
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52 | 44 | 1. _**Alpha**_: Adjust the level of regularization. |
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57 | 49 |
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58 | 50 | ### ElasticNet |
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62 | | -<figure><img src="../.gitbook/assets/image (4).png" alt="" width="563"><figcaption></figcaption></figure> |
| 52 | +<figure><img src="../.gitbook/assets/image (333).png" alt=""><figcaption></figcaption></figure> |
63 | 53 |
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64 | 54 | 1. _**Alpha**_: Adjust the level of regularization. |
65 | 55 | 2. _**L1 ratio**_: Adjusts the balance (ratio) between _**L1 (Lasso)**_ and _**L2 (Ridge)**_ regularization. |
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70 | 60 |
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71 | 61 | ### SVR(SupportVectorMachine Regressor) |
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75 | | -<figure><img src="../.gitbook/assets/image (5).png" alt="" width="563"><figcaption></figcaption></figure> |
| 63 | +<figure><img src="../.gitbook/assets/image (334).png" alt="" width="563"><figcaption></figcaption></figure> |
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77 | 65 | 1. _**C**_: Represents the degree of freedom for model regularization. Higher values of C make the model more complex, fitting the training data more closely. |
78 | 66 | 2. _**Kernel**_: Function mapping data to a higher-dimensional space, controlling model complexity. |
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80 | | -* _**Degree(Poly)**_: Determines the degree of polynomial. |
81 | | -* _**Gamma(Poly, rbf, sigmoid)**_: Adjusts the curvature of the decision boundary. |
82 | | -* _**Coef0(Poly, sigmoid)**_: Additional parameter for the kernel, controlling the offset. Higher values fit the training data more closely. |
83 | | - |
84 | | -3. _**Random state**_: Sets the seed value for the random number generator used in model training. |
| 67 | +3. _**Degree(Poly)**_: Determines the degree of polynomial. |
| 68 | +4. _**Gamma(Poly, rbf, sigmoid)**_: Adjusts the curvature of the decision boundary. |
| 69 | +5. _**Coef0(Poly, sigmoid)**_: Additional parameter for the kernel, controlling the offset. Higher values fit the training data more closely. |
| 70 | +6. _**Random state**_: Sets the seed value for the random number generator used in model training. |
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86 | 72 |
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87 | 73 |
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88 | 74 | *** |
89 | 75 |
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90 | 76 | ### DecisionTree Regressor |
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94 | | -<figure><img src="../.gitbook/assets/image (6).png" alt="" width="563"><figcaption></figcaption></figure> |
| 78 | +<figure><img src="../.gitbook/assets/image (335).png" alt="" width="563"><figcaption></figcaption></figure> |
95 | 79 |
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96 | 80 | 1. _**Criterion**_: Specifies the measure used for node splitting. |
97 | 81 | 2. _**Max depth**_: Specifies the maximum depth of the tree. |
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104 | 88 |
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105 | 89 | ### RandomForest Regressor |
106 | 90 |
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107 | | - |
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109 | | -<figure><img src="../.gitbook/assets/image (7).png" alt="" width="563"><figcaption></figcaption></figure> |
| 91 | +<figure><img src="../.gitbook/assets/image (336).png" alt="" width="563"><figcaption></figcaption></figure> |
110 | 92 |
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111 | 93 | 1. _**N estimators**_: Specifies the number of trees in the ensemble. |
112 | 94 | 2. _**Criterion**_: Specifies the measure used for node splitting. |
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121 | 103 |
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122 | 104 | ### GradientBoosting Regressor |
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124 | | - |
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126 | | -<figure><img src="../.gitbook/assets/image (8).png" alt="" width="563"><figcaption></figcaption></figure> |
| 106 | +<figure><img src="../.gitbook/assets/image (337).png" alt="" width="563"><figcaption></figcaption></figure> |
127 | 107 |
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128 | 108 | 1. _**Loss**_: Specifies the loss function used. |
129 | 109 | 2. _**Learning rate**_: Specifies the learning rate. |
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137 | 117 |
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138 | 118 | ### XGB Regressor |
139 | 119 |
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140 | | - |
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142 | | -<figure><img src="../.gitbook/assets/image (10).png" alt="" width="563"><figcaption></figcaption></figure> |
| 120 | +<figure><img src="../.gitbook/assets/image (338).png" alt=""><figcaption></figcaption></figure> |
143 | 121 |
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144 | 122 | 1. _**N estimators**_: Specifies the number of trees in the ensemble. |
145 | 123 | 2. _**Max depth**_: Specifies the maximum depth of the tree. |
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153 | 131 |
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154 | 132 | ### LGBM Regressor |
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156 | | - |
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158 | | -<figure><img src="../.gitbook/assets/image (11).png" alt="" width="563"><figcaption></figcaption></figure> |
| 134 | +<figure><img src="../.gitbook/assets/image (339).png" alt="" width="563"><figcaption></figcaption></figure> |
159 | 135 |
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160 | 136 | 1. _**Boosting type**_: Specifies the boosting type used in the algorithm. |
161 | 137 | 2. _**Max depth**_: Specifies the maximum depth of the tree. |
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169 | 145 |
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170 | 146 | ### CatBoost Regressor |
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172 | | - |
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174 | | -<figure><img src="../.gitbook/assets/image (12).png" alt="" width="563"><figcaption></figcaption></figure> |
| 148 | +<figure><img src="../.gitbook/assets/image (340).png" alt="" width="563"><figcaption></figcaption></figure> |
175 | 149 |
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176 | 150 | 1. _**Learning rate**_: Specifies the learning rate. |
177 | 151 | 2. _**Loss function**_: Specifies the loss function used. |
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