<figure><img src="../.gitbook/assets/image (153).png" alt="" width="211"><figcaption></figcaption></figure>

1. Click on the _**Classifier**_ under the _**Machine Learning**_ category.

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2. _**Model Type**_: Select the Model Type of the classifier you want to use:

* [Logistic Regression](6.-classifier.md#logistic-regression)

* BernoulliNB

* MultinomialNB

* GaussianNB

* [SVC(SupportVectorMachine Classifier)](6.-classifier.md#supportvectormachine-classifier)

* [DecisionTree Classifier](6.-classifier.md#decisiontree-classifier)

* [RandomForest Classifier](6.-classifier.md#randomforest-classifier)

* [GradientBoosting Classifier](6.-classifier.md#gradientboosting-classifier)

* [XGB Classifier](6.-classifier.md#xgb-classifier)

* [LGBM Classifier](6.-classifier.md#lgbm-classifier)

* [CatBoost Classifier](6.-classifier.md#catboost-classifier)

3. _**Allocate to**_: Specify the variable name to assign to the model.

4. _**Code View**_: Preview the generated code.

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

<figure><img src="../.gitbook/assets/image (155).png" alt="" width="563"><figcaption></figcaption></figure>

1. _**Penalty**_: Specify the regularization method for the model. (l2 / l1 / elasticnet / none)

2. _**C**_: Adjust the regularization strength.

3. _**Random State**_: Set the seed value for the random number generator.

<figure><img src="../.gitbook/assets/image (156).png" alt="" width="563"><figcaption></figcaption></figure>

1. _**C**_: C indicates the freedom of the model's regularization. A higher C value makes the model more complex to fit the training data.

2. _**Kernel**_: A function that maps data into higher dimensions. You can control the complexity of the model by selecting the kernel type.

* _**Degree (Poly)**_: Degree determines the degree of the polynomial. A higher degree increases the complexity of the model.

* _**Gamma (Poly, rbf, sigmoid)**_: Gamma adjusts the curvature of the decision boundary. A higher value makes the model fit the training data more closely.

* _**Coef0 (Poly, sigmoid)**_: An additional parameter for the kernel, controlling the offset of the kernel. A higher value makes the model fit the training data more closely.

3. _**Random State**_: Set the seed value for the random number generator.

<figure><img src="../.gitbook/assets/image (157).png" alt="" width="563"><figcaption></figcaption></figure>

1. _**Criterion**_: Specify the metric used to select the node split. (squared\_error / friedman\_mse / absolute\_error / Poisson)

2. _**Max Depth**_: Specify the maximum depth of the trees.

3. _**Min Samples Split**_: Specify the minimum number of samples required to split a node to prevent excessive splitting.

4. _**Random State**_: Set the seed value for the random number generator.

<figure><img src="../.gitbook/assets/image (158).png" alt="" width="563"><figcaption></figcaption></figure>

1. _**N estimators**_: Specify the number of trees to include in the ensemble.

2. _**Criterion**_: Specify the metric used to select the node split. Options include gini / entropy.

3. _**Max Depth**_: Specify the maximum depth of the trees.

4. _**Min Samples Split**_: Specify the minimum number of samples required to split a node to prevent excessive splitting.

5. _**N jobs**_: Specify the number of CPU cores or threads to use during model training for parallel processing.

6. _**Random State**_: Set the seed value for the random number generator.

<figure><img src="../.gitbook/assets/image (159).png" alt="" width="563"><figcaption></figcaption></figure>

1. _**Loss**_: Specify the loss function to be used. Options include deviance / exponential.

2. _**Learning rate**_: Adjust the contribution of each tree and the degree to which the errors of previous trees are corrected. A large value may lead to non-convergence or overfitting, while a small value may increase training time.

3. _**N estimators**_: Specify the number of trees to include in the ensemble.

4. _**Criterion**_: Specify the metric used to select the node split. (friedman\_mse / squared\_error / mse / mae)

5. _**Random State**_: Set the seed value for the random number generator.

<figure><img src="../.gitbook/assets/image (160).png" alt="" width="563"><figcaption></figcaption></figure>

1. _**N estimators**_: Specify the number of trees to include in the ensemble.

2. _**Max Depth**_: Specify the maximum depth of the trees.

3. _**Learning Rate**_: Adjust the contribution of each tree and the degree to which the errors of previous trees are corrected.

4. _**Gamma**_: Adjust the curvature of the decision boundary. A higher value makes the model fit the training data more closely.

5. _**Random State**_: Set the seed value for the random number generator.

<figure><img src="../.gitbook/assets/image (161).png" alt="" width="563"><figcaption></figcaption></figure>

1. _**Boosting type**_: Specify the boosting method used internally in the algorithm. (gbdt / dart / goss / rf (Random Forest))

2. _**Max Depth**_: Specify the maximum depth of the trees.

3. _**Learning rate**_: Adjust the contribution of each tree and the degree to which the errors of previous trees are corrected.

4. _**N estimators**_: Specify the number of trees to include in the ensemble.

5. _**Random State**_: Set the seed value for the random number generator.

<figure><img src="../.gitbook/assets/image (162).png" alt="" width="563"><figcaption></figcaption></figure>

1. _**Learning rate**_: Adjust the contribution of each tree and the degree to which the errors of previous trees are corrected.

2. _**Loss function**_: Specify the loss function to be used. (RMSE / absolute\_error / huber / quantile)

3. _**Task type**_: Specify the hardware used for data processing. (CPU / GPU)

4. _**Max depth**_: Specify the maximum depth of the trees.

5. _**N estimators**_: Specify the number of trees to include in the ensemble.

6. _**Random state**_: Set the seed value for the random number generator.