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GITBOOK-30: Dimension
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‎docs/machine-learning/5.-regressor.md

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<figure><img src="../.gitbook/assets/image.png" alt="" width="209"><figcaption></figcaption></figure>
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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.
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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.
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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.
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2. _**Kernel**_: Function mapping data to a higher-dimensional space, controlling model complexity.

‎docs/machine-learning/7.-clustering.md

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1. Click on _**Clustering**_ under the _**Machine Learning**_ category.
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2. _**Model type**_: Select the type of Model you want to use.
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* [KMeans / AgglomerativeClustering](7.-clustering.md#kmeans-agglomerativeclustering)
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1. _**N clusters**_: Specify the number of clusters to be generated.
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1. _**N components**_: Specify the number of Gaussian distributions to be used by the model to describe the data, determining how many clusters the data will be divided into.
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2. _**Random state**_: Set the seed value for the random number generator.
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1. _**Eps (Epsilon)**_: Specify the maximum distance (radius) for forming clusters.
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2. _**Min samples**_: Specify the minimum number of neighboring data points required for a point to be recognized as a cluster.

‎docs/machine-learning/8.-dimension.md

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# 8. Dimension
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<figure><img src="../.gitbook/assets/image (168).png" alt="" width="214"><figcaption></figcaption></figure>
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1. Click on _**Dimension**_ under the _**Machine Learning**_ category.
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2. _**Model type**_: Select the type of model.
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3. _**N components**_: Specify the desired number of dimensions to reduce the data to.
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4. _**Learning rate (TSNE)**_: Learning rate determines how much the TSNE algorithm reflects the distances between data points. Too large values may cause data to be excessively dense, while too small values may lead to convergence issues. Typically, values between 0.1 and 0.3 are used.
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5. _**Random state**_ - Set the seed value for the random number generator.
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6. _**Allocate to**_: Specify the variable name to assign to the model.
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7. _**Code view**_: Preview the generated code.
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8. _**Run**_: Execute the code.
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