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1. Click on _**AutoML**_ in the _**Machine Learning**_ category.

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2. _**Model Type**_: Choose the type of model to generate. You can conveniently install necessary packages through _**Install**_.

3. _**Generation(TPOT)**_: Set the number of generations to generate, evaluate, and compare. Each generation generates and evaluates candidate model pipelines. Higher values increase execution time.

4. _**Population Size(TPOT)**_: Determine the number of model candidates generated in each generation set in the previous step.

5. _**Cv(TPOT)**_: Determine the number of folds used for cross-validation.

6. _**Random State(TPOT)**_: Control randomness. If not set, different random sequences are generated each time.

7. _**Allocate to**_: Specify variable names to assign to the generated model.

8. _**Code View**_: Preview the code that will be output.

9. _**Run**_: Execute the code.

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

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2. _**Model Type**_: Choose the regression model.

* [Linear Regression](5.-regressor.md#linear-regression)

* [Ridge / Lasso](5.-regressor.md#ridge-lasso)

* [ElasticNet](5.-regressor.md#elasticnet)

* [SVR(SupportVectorMachine Regressor)](5.-regressor.md#svr-supportvectormachine-regressor)

* [DecisionTree Regressor](5.-regressor.md#decisiontree-regressor)

* [RandomForest Regressor](5.-regressor.md#randomforest-regressor)

* [GradientBoosting Regressor](5.-regressor.md#gradientboosting-regressor)

* [XGB Regressor](5.-regressor.md#xgb-regressor)

* [LGBM Regressor](5.-regressor.md#lgbm-regressor)

* [CatBoost Regressor](5.-regressor.md#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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1. _**Fit Intercept**_: Choose whether to include the intercept.

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1. _**Alpha**_: Adjust the level of regularization.

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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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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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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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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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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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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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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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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.

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