catboost | high performance Gradient Boosting on Decision Trees library | Machine Learning library
kandi X-RAY | catboost Summary
kandi X-RAY | catboost Summary
If you want to evaluate Catboost model in your application read model api documentation.
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catboost Key Features
catboost Examples and Code Snippets
Lines of Code : 23
License : No License
Copyimport xgboost
import shap
# train an XGBoost model
X, y = shap.datasets.boston()
model = xgboost.XGBRegressor().fit(X, y)
# explain the model's predictions using SHAP
# (same syntax works for LightGBM, CatBoost, scikit-learn, transformers, Spark, # The following code should work:
df.NACE_code = df.NACE_code.astype(str)
df.NACE_code = df.NACE_code.str.replace('.', '')
df['NACE_code'].astype('str').str.replace(r".", r"", regex=False)
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 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 y_pred_prob_lr=pipeline['lr'].predict_proba(X_test)
y_preds_proba_lr_df=pd.DataFrame(y_pred_prob_lr[:,1],columns=
["pred_default_proba"])
xg_cl=
xgb.XGBClassifier(objective='binary:logistic',n_estimators=10,seed=123)
xg_cl.fit(X_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.CatBoostClassifiabc = model.get_feature_importance(type=catBoost.EFstrType.FeatureImportance, prettified=True, thread_count=-1, verbose=False)
y_pred = model_cb.predict_proba(X_test)[:, 1]
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
Trending Discussions on catboost
QUESTION
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:
NACE_code
...ANSWER
Answered 2022-Mar-14 at 11:56Use astype('str') to convert columns to string type before calling str.replace.
Without regex:
QUESTION
ANSWER
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
QUESTION
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.
...ANSWER
Answered 2022-Feb-08 at 20:42The 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:
QUESTION
Does CatBoost regressor have a method to predict the probabilities of each prediction? I see one for CatBoost classifier (https://catboost.ai/en/docs/concepts/python-reference_catboostclassifier_predict_proba) but not for regressor.
...ANSWER
Answered 2022-Jan-21 at 06:47There is no predict_proba method in the Catboost regressor, but you can specify the output type when you call predict on the trained model.
QUESTION
I'm trying to install conda environment using the command:
...ANSWER
Answered 2021-Dec-22 at 18:02This 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 ModularizationI 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-buildshould be in base and only in basesnakemakeshould be in a dedicated environmentnotebook(i.e., Jupyter) should be in a dedicated environment, co-installed withnb_conda_kernels; all kernel environments need areipykernel
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.
QUESTION
Below is my catboost model:
...ANSWER
Answered 2021-Nov-24 at 09:56You 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
QUESTION
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:-
...ANSWER
Answered 2021-Nov-05 at 17:14Apparently, 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!!
QUESTION
I'm performing feature selection with Catbost. This the training code:
...ANSWER
Answered 2021-Oct-22 at 20:09The 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.
QUESTION
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":
https://github.com/catboost/catboost/blob/master/catboost/libs/model_interface/c_api.h#L175
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:
...ANSWER
Answered 2021-Oct-19 at 16:09The 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.
QUESTION
Here is the model:
...ANSWER
Answered 2021-Oct-06 at 01:21This was confirmed to be a weird error when using tidymodels for catboost. Check their github issues for more info and a current workaround.
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