🕷️ Crawler Inspector

[](https://catboost.ai/ "CatBoost") - Installation - [Overview](https://catboost.ai/docs/en/concepts/en/concepts/installation) - Python package installation - CatBoost for Apache Spark installation - R package installation - Command-line version binary - Build from source - Key Features - Training parameters - Python package - [Quick start](https://catboost.ai/docs/en/concepts/en/concepts/python-quickstart) - CatBoost - CatBoostClassifier - [Overview](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier) - [fit](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_fit) - [predict](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_predict) - [predict\_proba](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_predict_proba) - [Attributes](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_attributes) - [calc\_leaf\_indexes](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboost_calc_leaf_indexes) - [calc\_feature\_statistics](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_calc_feature_statistics) - [compare](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_modelcompare) - [copy](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_copy) - [eval\_metrics](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_eval-metrics) - [get\_all\_params](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_get_all_params) - [get\_best\_iteration](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_get_best_iteration) - [get\_best\_score](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_get_best_score) - [get\_borders](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_get_borders) - [get\_evals\_result](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_get_evals_result) - [get\_feature\_importance](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_get_feature_importance) - [get\_metadata](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_metadata) - [get\_object\_importance](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_get_object_importance) - [get\_param](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_get_param) - [get\_params](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_get_params) - [get\_probability\_threshold](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_get_probability_threshold) - [get\_scale\_and\_bias](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_get_scale_and_bias) - [get\_test\_eval](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_get_test_eval) - [grid\_search](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_grid_search) - [is\_fitted](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_is_fitted) - [load\_model](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_load_model) - [plot\_predictions](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_plot_predictions) - [plot\_tree](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_plot_tree) - [randomized\_search](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_randomized_search) - [save\_borders](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_save_borders) - [save\_model](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_save_model) - [score](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_score) - [select\_features](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_select_features) - [set\_feature\_names](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_set_feature_names) - [set\_params](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_set_params) - [set\_probability\_threshold](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_set_probability_threshold) - [set\_scale\_and\_bias](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_set_scale_and_bias) - [shrink](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_shrink) - [staged\_predict](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_staged_predict) - [staged\_predict\_proba](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_staged_predict_proba) - CatBoostRanker - CatBoostRegressor - [cv](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_cv) - datasets - FeaturesData - MetricVisualizer - Pool - [sample\_gaussian\_process](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_sample_gaussian_process) - [sum\_models](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_sum_models) - [to\_classifier](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_to_classifier) - [to\_regressor](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_to_regressor) - [train](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_train) - Text processing - utils - [Usage examples](https://catboost.ai/docs/en/concepts/en/concepts/python-usages-examples) - CatBoost for Apache Spark - R package - Command-line version - Applying models - Objectives and metrics - Model analysis - Data format description - [Parameter tuning](https://catboost.ai/docs/en/concepts/en/concepts/parameter-tuning) - [Speeding up the training](https://catboost.ai/docs/en/concepts/en/concepts/speed-up-training) - Data visualization - Algorithm details - [FAQ](https://catboost.ai/docs/en/concepts/en/concepts/faq) - Educational materials - [Development and contributions](https://catboost.ai/docs/en/concepts/en/concepts/development-and-contributions) - [Contacts](https://catboost.ai/docs/en/concepts/en/concepts/contacts) compare ## In this article: - [Method call format](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_modelcompare#compare__method-call-format) - [Parameters](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_modelcompare#parameters) - [log\_cout](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_modelcompare#log_cout) - [log\_cerr](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_modelcompare#log_cerr) - [Examples](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_modelcompare#examples) 1. Python package 2. [CatBoostClassifier](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier) 3. compare # compare - [Method call format](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_modelcompare#compare__method-call-format) - [Parameters](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_modelcompare#parameters) - [log\_cout](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_modelcompare#log_cout) - [log\_cerr](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_modelcompare#log_cerr) - [Examples](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_modelcompare#examples) Draw train and evaluation metrics in [Jupyter Notebook](https://catboost.ai/docs/en/concepts/en/features/visualization_jupyter-notebook) for two trained models. ## Method call format ``` compare(model, data=None, metrics=None, ntree_start=0, ntree_end=0, eval_period=1, thread_count=-1, tmp_dir=None, log_cout=sys.stdout, log_cerr=sys.stderr) ``` ## Parameters **Parameter:** `model` **Possible types:** CatBoost Model #### Description The CatBoost model to compare with. **Default value** Required parameter **Parameter:** `metrics`list of strings The list of metrics to be calculated. [Supported metrics](https://catboost.ai/docs/en/concepts/en/references/custom-metric__supported-metrics) For example, if the AUC and Logloss metrics should be calculated, use the following construction: ``` ['Logloss', 'AUC'] ``` Required parameter **Parameter:** `data` **Possible types:** catboost.Pool #### Description A file or matrix with the input dataset, on which the compared metric values should be calculated. **Default value** Required parameter **Parameter:** `ntree_start` **Possible types:** int #### Description To reduce the number of trees to use when the model is applied or the metrics are calculated, set the range of the tree indices to`[ntree_start; ntree_end)` and the `eval_period` parameter to *k* to calculate metrics on every *k*\-th iteration. This parameter defines the index of the first tree to be used when applying the model or calculating the metrics (the inclusive left border of the range). Indices are zero-based. **Default value** 0 **Parameter:** `ntree_end` **Possible types:** int #### Description To reduce the number of trees to use when the model is applied or the metrics are calculated, set the range of the tree indices to`[ntree_start; ntree_end)` and the `eval_period` parameter to *k* to calculate metrics on every *k*\-th iteration. This parameter defines the index of the first tree not to be used when applying the model or calculating the metrics (the exclusive right border of the range). Indices are zero-based. **Default value** 0 (the index of the last tree to use equals to the number of trees in the model minus one) **Parameter:** `eval_period` **Possible types:** int #### Description To reduce the number of trees to use when the model is applied or the metrics are calculated, set the range of the tree indices to`[ntree_start; ntree_end)` and the `eval_period` parameter to *k* to calculate metrics on every *k*\-th iteration. This parameter defines the step to iterate over the range `[`ntree\_start`;`ntree\_end`)`. For example, let's assume that the following parameter values are set: - `ntree_start` is set 0 - `ntree_end` is set to N (the total tree count) - `eval_period` is set to 2 In this case, the metrics are calculated for the following tree ranges: `[0, 2)`, `[0, 4)`, ... , `[0, N)` **Default value** 1 (the trees are applied sequentially: the first tree, then the first two trees, etc.) **Parameter:** `thread_count`int The number of threads to use. Optimizes the speed of execution. This parameter doesn't affect results. \-1 (the number of threads is equal to the number of processor cores) **Parameter:** `tmp_dir` **Possible types:** String #### Description The name of the temporary directory for intermediate results. **Default value** None (the name is generated) ### 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 ## Examples ``` from catboost import Pool, CatBoostClassifier train_data = [[0, 3], [4, 1], [8, 1], [9, 1]] train_labels = [0, 0, 1, 1] eval_data = [[1, 3], [4, 2], [8, 2], [8, 3]] eval_labels = [1, 0, 0, 1] train_dataset = Pool(train_data, train_labels) eval_dataset = Pool(eval_data, eval_labels) model1 = CatBoostClassifier(iterations=100, learning_rate=0.1) model1.fit(train_dataset, verbose=False) model2 = CatBoostClassifier(iterations=100, learning_rate=0.3) model2.fit(train_dataset, verbose=False) model1.compare(model2, eval_dataset, ['Logloss']) ``` The following is a chart plotted with [Jupyter Notebook](https://catboost.ai/docs/en/concepts/en/features/visualization_jupyter-notebook) for the given example.  ### Was the article helpful? Yes No Previous [calc\_feature\_statistics](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_calc_feature_statistics) Next [copy](https://catboost.ai/docs/en/concepts/en/concepts/python-reference_catboostclassifier_copy) 

Draw train and evaluation metrics in [Jupyter Notebook](https://catboost.ai/docs/en/features/visualization_jupyter-notebook) for two trained models. ## Method call format ``` compare(model, data=None, metrics=None, ntree_start=0, ntree_end=0, eval_period=1, thread_count=-1, tmp_dir=None, log_cout=sys.stdout, log_cerr=sys.stderr) ``` ## Parameters **Parameter:** `model` **Possible types:** CatBoost Model #### Description The CatBoost model to compare with. **Default value** Required parameter **Parameter:** `metrics`list of strings The list of metrics to be calculated. [Supported metrics](https://catboost.ai/docs/en/references/custom-metric__supported-metrics) For example, if the AUC and Logloss metrics should be calculated, use the following construction: ``` ['Logloss', 'AUC'] ``` Required parameter **Parameter:** `data` **Possible types:** catboost.Pool #### Description A file or matrix with the input dataset, on which the compared metric values should be calculated. **Default value** Required parameter **Parameter:** `ntree_start` **Possible types:** int #### Description To reduce the number of trees to use when the model is applied or the metrics are calculated, set the range of the tree indices to`[ntree_start; ntree_end)` and the `eval_period` parameter to *k* to calculate metrics on every *k*\-th iteration. This parameter defines the index of the first tree to be used when applying the model or calculating the metrics (the inclusive left border of the range). Indices are zero-based. **Default value** 0 **Parameter:** `ntree_end` **Possible types:** int #### Description To reduce the number of trees to use when the model is applied or the metrics are calculated, set the range of the tree indices to`[ntree_start; ntree_end)` and the `eval_period` parameter to *k* to calculate metrics on every *k*\-th iteration. This parameter defines the index of the first tree not to be used when applying the model or calculating the metrics (the exclusive right border of the range). Indices are zero-based. **Default value** 0 (the index of the last tree to use equals to the number of trees in the model minus one) **Parameter:** `eval_period` **Possible types:** int #### Description To reduce the number of trees to use when the model is applied or the metrics are calculated, set the range of the tree indices to`[ntree_start; ntree_end)` and the `eval_period` parameter to *k* to calculate metrics on every *k*\-th iteration. This parameter defines the step to iterate over the range `[`ntree\_start`;`ntree\_end`)`. For example, let's assume that the following parameter values are set: - `ntree_start` is set 0 - `ntree_end` is set to N (the total tree count) - `eval_period` is set to 2 In this case, the metrics are calculated for the following tree ranges: `[0, 2)`, `[0, 4)`, ... , `[0, N)` **Default value** 1 (the trees are applied sequentially: the first tree, then the first two trees, etc.) **Parameter:** `thread_count`int The number of threads to use. Optimizes the speed of execution. This parameter doesn't affect results. \-1 (the number of threads is equal to the number of processor cores) **Parameter:** `tmp_dir` **Possible types:** String #### Description The name of the temporary directory for intermediate results. **Default value** None (the name is generated) ### 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 ## Examples ``` from catboost import Pool, CatBoostClassifier train_data = [[0, 3], [4, 1], [8, 1], [9, 1]] train_labels = [0, 0, 1, 1] eval_data = [[1, 3], [4, 2], [8, 2], [8, 3]] eval_labels = [1, 0, 0, 1] train_dataset = Pool(train_data, train_labels) eval_dataset = Pool(eval_data, eval_labels) model1 = CatBoostClassifier(iterations=100, learning_rate=0.1) model1.fit(train_dataset, verbose=False) model2 = CatBoostClassifier(iterations=100, learning_rate=0.3) model2.fit(train_dataset, verbose=False) model1.compare(model2, eval_dataset, ['Logloss']) ``` The following is a chart plotted with [Jupyter Notebook](https://catboost.ai/docs/en/features/visualization_jupyter-notebook) for the given example.