--- metadata: - name: generator content: Diplodoc Platform v5.50.3 alternate: - https://catboost.ai/docs/en/concepts/python-reference_catboost_randomized_search.md --- > **Documentation Index:** Fetch the complete configuration index at https://catboost.ai/docs/en/llms.txt # randomized_search A simple randomized search on hyperparameters. In contrast to grid search, not all parameter values are tried out, but rather a fixed number of parameter settings is sampled from the specified distributions. The number of parameter settings that are tried is specified in the `n_iter` parameter. {% note info %} After searching, the model is trained and ready to use. {% endnote %} ## Method call format {#method-call-format} ```python randomized_search(param_distributions, X, y=None, cv=3, n_iter=10, partition_random_seed=0, calc_cv_statistics=True, search_by_train_test_split=True, refit=True, shuffle=True, stratified=None, train_size=0.8, verbose=True, log_cout=sys.stdout, log_cerr=sys.stderr) ``` ## Parameters {#parameters} ### param_distributions #### Description Dictionary with parameters names (string) as keys and distributions or lists of parameter settings to try. Distributions must provide a `rvs` method for sampling (such as those from `scipy.stats.distributions`). If a list is given, it is sampled uniformly. **Possible types:** dict **Default value** Required parameter ### X #### Description The description is different for each group of possible types. **Possible types** {% cut "catboost.Pool" %} The input training dataset. {% note info %} If a nontrivial value of the cat_features parameter is specified in the constructor of this class, CatBoost checks the equivalence of categorical features indices specification from the constructor parameters and in this Pool class. {% endnote %} {% endcut %} {% cut "numpy.ndarray, pandas.DataFrame, polars.DataFrame" %} The input training dataset in the form of a two-dimensional feature matrix. {% endcut %} {% cut "pandas.SparseDataFrame, scipy.sparse.spmatrix (all subclasses except dia_matrix)" %} The input training dataset in the form of a two-dimensional sparse feature matrix. {% endcut %} **Default value** Required parameter ### y #### Description The target variables (in other words, the objects' label values) for the training dataset. Must be in the form of a one- or two- dimensional array. The type of data in the array depends on the machine learning task being solved: - Regression and ranking — One-dimensional array of numeric values. - Multiregression - Two-dimensional array of numeric values. The first index is for a dimension, the second index is for an object. - Binary classification One-dimensional array containing one of: * Booleans, integers or strings that represent the labels of the classes (only two unique values). * Numeric values. The interpretation of numeric values depends on the selected loss function: - Logloss — The value is considered a positive class if it is strictly greater than the value of the `target_border` training parameter. Otherwise, it is considered a negative class. - CrossEntropy — The value is interpreted as the probability that the dataset object belongs to the positive class. Possible values are in the range `[0; 1]`. - Multiclassification — One-dimensional array of integers or strings that represent the labels of the classes. - Multi label classification Two-dimensional array. The first index is for a label/class, the second index is for an object. Possible values depend on the selected loss function: * MultiLogloss — Only {0, 1} or {False, True} values are allowed that specify whether an object belongs to the class corresponding to the first index. * MultiCrossEntropy — Numerical values in the range `[0; 1]` that are interpreted as the probability that the dataset object belongs to the class corresponding to the first index. {% note info %} Do not use this parameter if the input training dataset (specified in the X parameter) type is catboost.Pool. {% endnote %} **Possible types** - list - numpy.ndarray - pandas.DataFrame - pandas.Series - [polars.Series](https://docs.pola.rs/api/python/stable/reference/series/index.html) - [polars.DataFrame](https://docs.pola.rs/api/python/stable/reference/dataframe/index.html) **Default value** None **Supported processing units** CPU and GPU ### cv #### Description The cross-validation splitting strategy. The interpretation of this parameter depends on the input data type: - None — Use the default three-fold cross-validation. - int — The number of folds in a (Stratified)KFold - object — One of the scikit-learn Splitter Classes with the split method. - An iterable yielding train and test splits as arrays of indices. **Possible types** - int - scikit-learn splitter object - cross-validation generator - iterable **Default value** None ### n_iter #### Description The number of parameter settings that are sampled. This parameter trades off runtime vs quality of the solution. **Possible types** int **Default value** 0 ### partition_random_seed #### Description Use this as the seed value for random permutation of the data. The permutation is performed before splitting the data for cross-validation. Each seed generates unique data splits. **Possible types** int **Default value** 0 ### calc_cv_statistics #### Description Estimate the quality by using cross-validation with the best of the found parameters. The model is fitted using these parameters. This option can be enabled if the `search_by_train_test_split` parameter is set to True. **Possible types** bool **Default value** True ### search_by_train_test_split #### Description Split the source dataset into train and test parts. Models are trained on the train part, while parameters are compared by the loss function score on the test dataset. It is recommended to enable this option for large datasets and disable it for the small ones. **Possible types** bool **Default value** True ### refit #### Description Refit an estimator using the best-found parameters on the whole dataset. **Possible types** bool **Default value** True ### shuffle #### Description Shuffle the dataset objects before splitting into folds. **Possible types** bool **Default value** True ### stratified #### Description Perform stratified sampling. True for classification and False otherwise. **Possible types** bool **Default value** None ### train_size #### Description The proportion of the dataset to include in the train split. Possible values are in the range [0;1]. **Possible types** float **Default value** 0.8 ### verbose #### Description The purpose of this parameter depends on the type of the given value: - int — The frequency of iterations to print the information to stdout. - bool — Print the information to stdout on every iteration (if set to “True”) or disable any logging (if set to “False”). **Possible types** - bool - int **Default value** True ### plot #### Description Draw train and evaluation metrics for every set of parameters in Jupyter [Jupyter Notebook](https://catboost.ai/docs/en/features/visualization_jupyter-notebook.md). **Possible types** bool **Default value** False ### log_cout Output stream or callback for logging. **Possible types** - callable Python object - python object providing the `write()` method **Default value** sys.stdout ### log_cerr Error stream or callback for logging. **Possible types** - callable Python object - python object providing the `write()` method **Default value** sys.stderr ## Return value {#output-format} Dict with two fields: - `params` — `dict` of best-found parameters. - `cv_results` — `dict` or pandas.core.frame.DataFrame with cross-validation results. Сolumns are: `test-error-mean`, `test-error-std`, `train-error-mean`, `train-error-std`. ## Examples {#example} ```python from catboost import CatBoost train_data = [[1, 4, 5, 6], [4, 5, 6, 7], [30, 40, 50, 60], [20, 30, 70, 60], [10, 80, 40, 30], [10, 10, 20, 30]] train_labels = [10, 20, 30, 15, 10, 25] model = CatBoost() grid = {'learning_rate': [0.03, 0.1], 'depth': [4, 6, 10], 'l2_leaf_reg': [1, 3, 5, 7, 9]} randomized_search_result = model.randomized_search(grid, X=train_data, y=train_labels, plot=True) ``` The following is a chart plotted with [Jupyter Notebook](https://catboost.ai/docs/en/features/visualization_jupyter-notebook.md) for the given example. ![](../images/interface__catboost__randomized_search.png)