--- metadata: - name: generator content: Diplodoc Platform v5.52.0 alternate: - https://catboost.ai/docs/en/concepts/python-reference_sample_gaussian_process.md - href: en/concepts/python-reference_sample_gaussian_process.md type: text/markdown title: Markdown version - href: ../llms.txt type: text/markdown title: llms.txt --- > **Documentation Index:** Fetch the complete configuration index at https://catboost.ai/docs/en/llms.txt # sample_gaussian_process ## Purpose {#purpose} Implementation of Gaussian process sampling (Kernel Gradient Boosting/Algorithm 4) from ["Gradient Boosting Performs Gaussian Process Inference"](https://arxiv.org/abs/2206.05608) paper. Produces samples from posterior GP with prior assumption $f \sim \mathcal{GP}(0, \sigma^2 \mathcal{K} + \delta^2 I)$ ## Method call format {#call-format} ```python sample_gaussian_process(X, y, eval_set=None, cat_features=None, text_features=None, embedding_features=None, random_seed=None, samples=10, posterior_iterations=900, prior_iterations=100, learning_rate=0.1, depth=6, sigma=0.1, delta=0, random_strength=0.1, random_score_type='Gumbel', eps=1e-4, verbose=False) ``` ## Parameters {#parameters} ### X #### Description Training data with features. Must be non-empty (contain > 0 objects) **Possible types** {% cut "list, numpy.ndarray, pandas.DataFrame, pandas.Series, polars.DataFrame" %} Two-dimensional feature matrix. {% endcut %} {% cut "pandas.SparseDataFrame, scipy.sparse.spmatrix (all subclasses except dia_matrix)" %} Two-dimensional sparse feature matrix. {% endcut %} {% cut "catboost.FeaturesData" %} Special class for features data. See [FeaturesData](https://catboost.ai/docs/en/concepts/python-features-data__desc.md). {% endcut %} **Default value** Required parameter ### y #### Description Labels of the training data. Must be a single-dimensional array with numerical values. **Possible types** - list - numpy.ndarray - pandas.Series **Default value** Required parameter ### eval_set #### Description The validation dataset or datasets used for the following processes in the posterior fitting: - [overfitting detector](https://catboost.ai/docs/en/concepts/overfitting-detector.md) - monitoring metrics' changes **Possible types** - catboost.Pool - list of catboost.Pool - tuple (X, y) - list of tuples (X, y) - string (path to the dataset file) - list of strings (paths to dataset files) **Default value** None ### cat_features #### Description A one-dimensional array of categorical features columns indices. **Possible types** - list - numpy.ndarray **Default value** None (all features are either considered numerical or of other types if specified precisely) ### text_features #### Description A one-dimensional array of text features columns indices. **Possible types** - list - numpy.ndarray **Default value** None (all features are either considered numerical or of other types if specified precisely) ### embedding_features #### Description A one-dimensional array of embedding features columns indices. **Possible types** - list - numpy.ndarray **Default value** None (all features are either considered numerical or of other types if specified precisely) ### random_seed #### Description The random seed used for training. Possible range is [0, +inf) **Possible types** - int **Default value** None. 0 is used in this case. ### samples #### Description Number of Monte-Carlo samples from GP posterior. Controls how many models this function will return. Possible range is [1, +inf) **Possible types** - int **Default value** 10 ### posterior_iterations #### Description Max count of trees for posterior sampling step. Possible range is \[1, +inf\) **Possible types** - int **Default value** 900 ### prior_iterations #### Description Max count of trees for prior sampling step. Possible range is \[1, +inf\) **Possible types** - int **Default value** 100 ### learning_rate #### Description Step size shrinkage used in update to prevent overfitting. Possible range is \(0, 1\] **Possible types** - float **Default value** 0.1 ### depth #### Description Depth of the trees in the models. Possible range is \[1, 16\] **Possible types** - int **Default value** 6 ### sigma #### Description Scale of GP kernel (lower values lead to lower posterior variance). Possible range is \(0, +inf\) **Possible types** - float **Default value** 0.1 ### delta #### Description Scale of homogenious noise of GP kernel (adjust if target is noisy) Possible range is \[0, +inf\) **Possible types** - float **Default value** 0.0 ### random_strength #### Description Corresponds to parameter `beta` in the paper. Higher values lead to faster convergence to GP posterior. Possible range is \(0, +inf\) **Possible types** - float **Default value** 0.1 ### random_score_type #### Description Type of random noise added to scores. Possible values: - `Gumbel` - Gumbel-distributed (as in paper) - `NormalWithModelSizeDecrease` - Normally-distributed with deviation decreasing with model iteration count (default in CatBoost) **Possible types** - string **Default value** - `Gumbel` ### eps #### Description Technical parameter that controls the precision of prior estimation. Possible range is \(0, 1\] **Possible types** - float **Default value** 1.e-4 ### verbose #### Description Verbosity of posterior model training output If `verbose` is `bool`, then if set to `True`, `logging_level` is set to `Verbose`, if set to `False`, `logging_level` is set to `Silent`. If `verbose` is `int`, it determines the frequency of writing metrics to output and `logging_level` is set to `Verbose`. **Possible types** - bool - int **Default value** `False` ## Return value {#output-format} List of trained [CatBoostRegressor](https://catboost.ai/docs/en/concepts/python-reference_catboostregressor.md) models (size = [samples parameter](#samples) value).