5. Regressor

1. Click on the Regressor in the Machine Learning category.

2. Model Type: Choose the regression model.

• Linear Regression

• Ridge / Lasso

• ElasticNet

• SVR(SupportVectorMachine Regressor)

• DecisionTree Regressor

• RandomForest Regressor

• GradientBoosting Regressor

• XGB Regressor

• LGBM Regressor

• CatBoost Regressor

3. Allocate to: Enter the variable name to assign to the created machine learning model.

4. Code View: Preview the generated code.

5. Run: Execute the code.

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Linear Regression

1. Fit Intercept: Choose whether to include the intercept.

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Ridge / Lasso

1. Alpha: Adjust the level of regularization.

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ElasticNet

1. Alpha: Adjust the level of regularization.

2. L1 ratio: Adjusts the balance (ratio) between L1 (Lasso) and L2 (Ridge) regularization.

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SVR(SupportVectorMachine Regressor)

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.

2. Kernel: Function mapping data to a higher-dimensional space, controlling model complexity.

• Degree(Poly): Determines the degree of polynomial.

• Gamma(Poly, rbf, sigmoid): Adjusts the curvature of the decision boundary.

• Coef0(Poly, sigmoid): Additional parameter for the kernel, controlling the offset. Higher values fit the training data more closely.

3. Random state: Sets the seed value for the random number generator used in model training.

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DecisionTree Regressor

1. Criterion: Specifies the measure used for node splitting.

2. Max depth: Specifies the maximum depth of the tree.

3. Min Samples Split: Specifies the minimum number of samples required to split a node.

4. Random state: Sets the seed value for the random number generator used in model training.

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RandomForest Regressor

1. N estimators: Specifies the number of trees in the ensemble.

2. Criterion: Specifies the measure used for node splitting.

3. Max depth: Specifies the maximum depth of the tree.

4. Min Samples Split: Specifies the minimum number of samples required to split a node.

5. N jobs: Specifies the number of CPU cores or threads to be used during model training.

6. Random State: Sets the seed value for the random number generator used in model training.

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GradientBoosting Regressor

1. Loss: Specifies the loss function used.

2. Learning rate: Specifies the learning rate.

3. N estimators: Specifies the number of trees in the ensemble.

4. Criterion: Specifies the measure used for node splitting.

5. Random State: Sets the seed value for the random number generator used in model training.

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XGB Regressor

1. N estimators: Specifies the number of trees in the ensemble.

2. Max depth: Specifies the maximum depth of the tree.

3. Learning rate: Specifies the learning rate.

4. Gamma: Specifies the minimum loss reduction required to make a further partition.

5. Random State: Sets the seed value for the random number generator used in model training.

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LGBM Regressor

1. Boosting type: Specifies the boosting type used in the algorithm.

2. Max depth: Specifies the maximum depth of the tree.

3. Learning Rate: Specifies the learning rate.

4. N estimators: Specifies the number of trees in the ensemble.

5. Random State: Sets the seed value for the random number generator used in model training.

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CatBoost Regressor

1. Learning rate: Specifies the learning rate.

2. Loss function: Specifies the loss function used.

3. Task Type: Specifies the hardware used for data processing.

4. Max Depth: Specifies the maximum depth of the tree.

5. N estimators: Specifies the number of trees in the ensemble.

6. Random State: Sets the seed value for the random number generator used in model training.