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

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

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

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

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

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.

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.

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.

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.

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

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

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.

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)