catboost | high performance Gradient Boosting on Decision Trees library | Machine Learning library

 by   catboost Python Version: 1.2.5 License: Apache-2.0

kandi X-RAY | catboost Summary

kandi X-RAY | catboost Summary

catboost is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch applications. catboost has no bugs, it has no vulnerabilities, it has a Permissive License and it has medium support. However catboost build file is not available. You can download it from GitHub, Maven.

If you want to evaluate Catboost model in your application read model api documentation.

            kandi-support Support

              catboost has a medium active ecosystem.
              It has 7188 star(s) with 1127 fork(s). There are 193 watchers for this library.
              There were 2 major release(s) in the last 6 months.
              There are 486 open issues and 1587 have been closed. On average issues are closed in 125 days. There are 14 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of catboost is 1.2.5

            kandi-Quality Quality

              catboost has no bugs reported.

            kandi-Security Security

              catboost has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

            kandi-License License

              catboost is licensed under the Apache-2.0 License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              catboost releases are available to install and integrate.
              Deployable package is available in Maven.
              catboost has no build file. You will be need to create the build yourself to build the component from source.
              Installation instructions are available. Examples and code snippets are not available.

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            catboost Key Features

            No Key Features are available at this moment for catboost.

            catboost Examples and Code Snippets

            copy iconCopy
            import xgboost
            import shap
            # train an XGBoost model
            X, y =
            model = xgboost.XGBRegressor().fit(X, y)
            # explain the model's predictions using SHAP
            # (same syntax works for LightGBM, CatBoost, scikit-learn, transformers, Spark,   
            Dataframe: How to remove dot in a string
            Pythondot img2Lines of Code : 4dot img2License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            # The following code should work:
            df.NACE_code = df.NACE_code.astype(str)
            df.NACE_code = df.NACE_code.str.replace('.', '')
            Dataframe: How to remove dot in a string
            Pythondot img3Lines of Code : 2dot img3License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            df['NACE_code'].astype('str').str.replace(r".", r"", regex=False)
            Label encode then impute missing then inverse encoding
            Pythondot img4Lines of Code : 43dot img4License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            import numpy as np
            import sklearn
            from sklearn.datasets import make_classification
            from sklearn.model_selection import train_test_split
            from sklearn.ensemble import RandomForestClassifier
            #from catboost import CatBoostClassifier
            # create 
            How to specify more than one eval_metric for a CatBoostRegressor?
            Pythondot img5Lines of Code : 28dot img5License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            from catboost import CatBoostRegressor
            from sklearn.datasets import make_regression
            from sklearn.model_selection import train_test_split
            # generate the data
            X, y = make_regression(n_samples=100, n_features=10, random_state=0)
            # split the
            Code for probability calibration for classification
            Pythondot img6Lines of Code : 38dot img6License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            CatBoost -- suppressing iteration results in a grid search
            Pythondot img7Lines of Code : 27dot img7License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            import catboost
            from sklearn.datasets import make_classification
            from scipy import stats
            # generate some data
            X, y = make_classification(n_features=10)
            # instantiate the model with logging_level='Silent'
            model = catboost.CatBoostClassifi
            Catboost python feature importance missing 1 required positional argument: 'value'
            Pythondot img8Lines of Code : 2dot img8License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            abc = model.get_feature_importance(type=catBoost.EFstrType.FeatureImportance, prettified=True, thread_count=-1, verbose=False)
            Why do my CatBoost fit metrics are different than the sklearn evaluation metrics?
            Pythondot img9Lines of Code : 2dot img9License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            y_pred = model_cb.predict_proba(X_test)[:, 1]
            Load Saved CatBoost Model (.cbm) from Google Cloud Storage
            Pythondot img10Lines of Code : 22dot img10License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            from io import BytesIO
            storage_client = storage.Client()
            # Storage variables
            model_bucket_id = #Replace with your bucket ID
            model_bucket = storage_client.get_bucket(model_bucket_id)
            model_name = #Replace with the file name of the model

            Community Discussions


            Dataframe: How to remove dot in a string
            Asked 2022-Mar-14 at 12:17

            I want to use categorical features directly with CatBoost model and I need to declare my object columns as categorical in Catboost model . I have a column in my data frame which is an object containing nace codes looking like this:




            Answered 2022-Mar-14 at 11:56

            Use astype('str') to convert columns to string type before calling str.replace.

            Without regex:



            How to plot a horizontal Stacked bar plot using Plotly-Python?
            Asked 2022-Feb-09 at 16:52

            I'm trying to plot the below summary metric plot using plotly.




            Answered 2022-Feb-09 at 16:52
            • shape the data frame first df2 = df.set_index("Model").unstack().to_frame().reset_index()
            • then it's a simple case of using Plotly Express



            Number of configurations in mlr3 hyperband tuning
            Asked 2022-Feb-08 at 20:42

            How can I control the number of configurations being evaluated during hyperband tuning in mlr3? I noticed that when I tune 6 parameters in xgboost(), the code evaluates about 9 configurations. When I tune the same number of parameters in catboost(), the code starts with evaluating 729 configurations. I am using eta = 3 in both cases.



            Answered 2022-Feb-08 at 20:42

            The number of sampled configurations in hyperband is defined by the lower and upper bound of the budget hyperparameter and eta. You can get a preview of the schedule and number of configurations:



            Predicting probabilities in CatBoost regressor
            Asked 2022-Jan-21 at 06:47

            Does CatBoost regressor have a method to predict the probabilities of each prediction? I see one for CatBoost classifier ( but not for regressor.



            Answered 2022-Jan-21 at 06:47

            There is no predict_proba method in the Catboost regressor, but you can specify the output type when you call predict on the trained model.



            Solving conda environment stuck
            Asked 2021-Dec-22 at 18:02

            I'm trying to install conda environment using the command:



            Answered 2021-Dec-22 at 18:02

            This solves fine (), but is indeed a complex solve mainly due to:

            • underspecification
            • lack of modularization

            This particular environment specification ends up installing well over 300 packages. And there isn't a single one of those that are constrained by the specification. That is a huge SAT problem to solve and Conda will struggle with this. Mamba will help solve faster, but providing additional constraints can vastly reduce the solution space.

            At minimum, specify a Python version (major.minor), such as python=3.9. This is the single most effective constraint.

            Beyond that, putting minimum requirements on central packages (those that are dependencies of others) can help, such as minimum NumPy.

            Lack of Modularization

            I assume the name "devenv" means this is a development environment. So, I get that one wants all these tools immediately at hand. However, Conda environment activation is so simple, and most IDE tooling these days (Spyder, VSCode, Jupyter) encourages separation of infrastructure and the execution kernel. Being more thoughtful about how environments (emphasis on the plural) are organized and work together, can go a long way in having a sustainable and painless data science workflow.

            The environment at hand has multiple red flags in my book:

            • conda-build should be in base and only in base
            • snakemake should be in a dedicated environment
            • notebook (i.e., Jupyter) should be in a dedicated environment, co-installed with nb_conda_kernels; all kernel environments need are ipykernel

            I'd probably also have the linting/formatting packages separated, but that's less an issue. The real killer though is snakemake - it's just a massive piece of infrastructure and I'd strongly encourage keeping that separated.



            Very strange accuracy change?
            Asked 2021-Nov-24 at 15:00

            Below is my catboost model:



            Answered 2021-Nov-24 at 09:56

            You are using the wrong metric. r2_score calculates r squared or coefficient of determination, this is used for regression and not for calculating accuracy.

            You should use accuracy score



            Unable to tune hyperparameters for CatBoostRegressor
            Asked 2021-Nov-05 at 17:14

            I am trying to fit a CatBoostRegressor to my model. When I perform K fold CV for the baseline model everything works fine. But when I use Optuna for hyperparameter tuning, it does something really weird. It runs the first trial and then throws the following error:-



            Answered 2021-Nov-05 at 17:14

            Apparently, CatBoost has this mechanism where you have to create new CatBoost model object for each trial. I opened an issue on Github regarding this and they said it was implemented to to protect results of a long training. which makes no sense to me!

            As of right now, the only workaround to this issue is you HAVE to create new CatBoost models for each and every trial!

            The other much sensible way, if you are using Pipeline method and Optuna, is to define the final pipeline instance and the model instance in the optuna function. And then again define the final pipeline instance outside the function.

            That way you do not have to define 50 instances if you are using 50 trials!!



            What is causing this discrepancy between the metric displayed at Catboost.select_features's plot and the actual predictions of the fitted final model?
            Asked 2021-Oct-22 at 20:17

            I'm performing feature selection with Catbost. This the training code:



            Answered 2021-Oct-22 at 20:09

            The solution to this question can be found at: CatBoost precision imbalanced classes

            After setting the parameter sample_weights of the sklearn's f1_score(), i got the same F1 score than Catboost was throwing.



            Passing a Go slice of float32 to CGo as a C float[]
            Asked 2021-Oct-19 at 16:52

            I'm trying to use catboost to predict for one array of floats.

            In the documentation for CalcModelPredictionSingle it takes as param "floatFeatures - array of float features":

            However when I try to pass an array of floats, I get this error:

            Cannot use type []*_Ctype_float as *_Ctype_float in assignment.

            Indicating it's expecting a single float. Am I using the wrong function?

            I am using cgo and this is part of my code:



            Answered 2021-Oct-19 at 16:09

            The API is expecting an array of floats, but []*C.float that you're trying to make is an array of pointers-to-floats. Those are incompatible types, which is exactly what the compiler is telling.

            The good news is none of that is necessary as a Go []float32 is layout-compatible with a C float[] so you can pass your Go slice directly to the C function as a pointer to its first element.



            Trying to use tidymodels for a catboost model: Receiving error related to labels
            Asked 2021-Oct-14 at 07:07

            Here is the model:



            Answered 2021-Oct-06 at 01:21

            This was confirmed to be a weird error when using tidymodels for catboost. Check their github issues for more info and a current workaround.


            Community Discussions, Code Snippets contain sources that include Stack Exchange Network


            No vulnerabilities reported

            Install catboost

            All CatBoost documentation is available here. Install CatBoost by following the guide for the.
            Python package
            command line
            Package for Apache Spark
            Training modes and metrics
            Parameters tuning
            Feature importance calculation
            Regular and staged predictions


            All CatBoost documentation is available here. Install CatBoost by following the guide for the.
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