1. Click on _**Data Sets**_ in the _**Machine Learning**_ category.

1. Click on _**Fit/Predict**_ under the _**Machine Learning**_ category.

1. Click on _**Model Info**_ in the _**Machine Learning**_ category.

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

1. _**Target Data**_: Specify the target data.

2. _**Predict Data**_: Specify the data to predict.

1. _**Clustered Index**_: Load the data containing index information assigned to the original data by clusters.

2. _**Feature Data**_: Load the original data. The _**Silhouette Score**_ is derived through computations with the data specified in the _**Clustered Index**_.

1. Click on _**Save/Load**_ in the _**Machine Learning**_ category.

1. Click on _**Data Split**_ in the _**Machine Learning**_ category.

1. Click on **Data Prep** in the **Machine Learning** category.

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

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

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

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

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

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

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

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. Click on _**Clustering**_ under the _**Machine Learning**_ category.

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

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