An open-source, low-code machine learning library in Python
Modules Impacted: pycaret.classification
pycaret.regression
pycaret.clustering
pycaret.anomaly
pycaret.arules
pycaret.nlp
pycaret.regression
module. You can now generate interactive residual plots by using residuals_interactive
in the plot_model
function.display_format
is added in the plot_model
function. To render plot in streamlit app, set this to streamlit
.tune_model
in pycaret.classification
and pycaret.regression
is now compatible with custom models.pip install pycaret[full]
.raw_score
argument in the predict_model
function for pycaret.classification
module. When set to True, scores for each class will be returned separately.handle_unknown_categorical
is set to False in the setup
function, an exception will be raised during prediction if the data contains unknown levels in categorical features.predict_model
for multiclass classification now returns labels as an integer.pycaret. clustering
and pycaret. anomaly
pycaret.classification
and pycaret.regression
.logs.log
file cannot be created when setup
is initialized, no exception will be raised now (support for more configurable logging to come in the future)Release: PyCaret 2.2.3 | Release Date: December 22, 2020 (SEVERAL BUGS FIX | CRITICAL COMPATIBILITY FIX)
predict_model
function when data columns had non-string characters.remove_multicollinearity
parameter in the setup
function`.models
function when the type
parameter was passed.pull
function.requirements.txt
.optimize_threshold
function the pycaret.classification
module. It now returns a float instead of an array.predict_model
function. It now uses the original data frame to append the predictions. As such any extra columns given at the time of inference are not removed when returning the predictions. Instead they are internally ignored at the time of predictions.create_model
function in pycaret.clustering
.pycaret.regression
when transform_target
is True in the setup
function.models
function if the type
parameter is specified.Post-release 2.2
, the following issues have been fixed:
plot_model = 'tree'
exceptions.predict_model
causing errors with non-contiguous indices.remove_outliers
parameter in the setup
function. It was introducing extra columns in training data. The issue has been fixed now.plot_model
in pycaret.clustering
causing errors with non-contiguous indices.imputation_type
is set to 'iterative' in the setup
function.compare_models
now prints intermediate output when html=False
.pycaret.classification
for binary classification are now calculated with average='binary'
. Before they were a weighted average of positive and negative class, now they are just calculated for positive class. For multiclass classification average='weighted'
.optimize_threshold
now returns optimized probability threshold value as numpy object.compare_models
.profile_kwargs
argument in the setup
function to pass keyword arguments to Pandas Profiler.plot_model
, interpret_model
, and evaluate_model
now accepts a new parameter use_train_data
which when set to True, generates plot on train data instead of test data.Modules Impacted: pycaret.classification
pycaret.regression
pycaret.clustering
pycaret.anomaly
Separate Train and Test Set: New parameter test_data
has been added in the setup
function of pycaret.classification
and pycaret.regression
. When a DataFrame is passed into the test_data
, it is used as a holdout set and the train_size
parameter is ignored. test_data
must be labeled and the shape of test_data
must match with the shape of data
.
Disable Default Preprocessing: A new parameter preprocess
has been added into the setup
function. When preprocess
is set to False
, no transformations are applied except for train_test_split
and custom transformations passed in the custom_pipeline
param. Data must be ready for modeling (no missing values, no dates, categorical data encoding) when preprocess is set to False.
Custom Metrics: New functions get_metric
, add_metric
and remove_metric
is now added in pycaret.classification
, pycaret.regression
, and pycaret.clustering
, that can be used to add / remove metrics used in model evaluation.
Custom Transformations: A new parameter custom_pipeline
has been added into the setup
function. It takes a tuple of (str, transformer)
or a list of tuples. When passed, it will append the custom transformers in the preprocessing pipeline and are applied on each CV fold separately and on the final fit. All the custom transformations are applied after train_test_split
and before pycaret's internal transformations.
GPU enabled Training: To use GPU for training use_gpu
parameter in the setup
function can be set to True
or force
. When set to True, it will use GPU with algorithms that support it and fall back on CPU for remaining. When set to force
it will only use GPU-enabled algorithms and raise exceptions if they are unavailable for use. The following algorithms are supported on GPU:
pycaret.classification
pycaret.regression
pycaret.classification
pycaret.regression
pycaret.classification
pycaret.regression
pycaret.classification
pycaret.regression
pycaret.classification
pycaret.regression
pycaret.classification
pycaret.regression
pycaret.classification
pycaret.classification
pycaret.regression
pycaret.regression
pycaret.regression
pycaret.regression
pycaret.clustering
pycaret.clustering
Hyperparameter Tuning: New methods for hyperparameter tuning has been added in the tune_model
function for pycaret.classification
and pycaret.regression
. New parameter search_library
and search_algorithm
in the tune_model
function is added. search_library
can be scikit-learn
, scikit-optimize
, tune-sklearn
, and optuna
. The search_algorithm
param can take the following values based on its search_library
:
random
grid
bayesian
random
grid
bayesian
hyperopt
bohb
random
tpe
Except for scikit-learn
, all the other search libraries are not hard dependencies of pycaret and must be installed separately.
Early Stopping: Early stopping now supported for hyperparameter tuning. A new parameter early_stopping
is added in the tune_model
function for pycaret.classification
and pycaret.regression
. It is ignored when search_library
is scikit-learn
, or if the estimator doesn't have a 'partial_fit' attribute. It can be either an object accepted by the search library or one of the following:
asha
for Asynchronous Successive Halving Algorithmhyperband
for Hyperbandmedian
for median stopping ruleFalse
or None
, early stopping will not be used.Iterative Imputation: Iterative imputation type for numeric and categorical missing values is now implemented. New parameters imputation_type
, iterative_imptutation_iters
, categorical_iterative_imputer
, and numeric_iterative_imputer
added in the setup
function. Read the blog post for more details: https://www.linkedin.com/pulse/iterative-imputation-pycaret-22-antoni-baum/?trackingId=Shg1zF%2F%2FR5BE7XFpzfTHkA%3D%3D
New Plots: Following new plots have been added:
pycaret.classification
pycaret.classification
pycaret.classification
pycaret.regression
pycaret.classification
pycaret.regression
CatBoost Compatibility: CatBoostClassifier
and CatBoostRegressor
is now compatible with plot_model
. It requires catboost>=0.23.2
.
Log Plots in MLFlow Server: You can now log any plot in the MLFlow
tracking server that is available in the plot_model
function. To log specific plots, pass a list containing plot IDs in the log_plots
parameter. Check the documentation of the plot_model
to see all available plots.
Data Split Stratification: A new parameter data_split_stratify
is added in the setup
function of pycaret.classification
and pycaret.regression
. It controls stratification during train_test_split
. When set to True, will stratify by target column. To stratify on any other columns, pass a list of column names.
Fold Strategy: A new parameter fold_strategy
is added in the setup
function for pycaret.classification
and pycaret.regression
. By default, it is 'stratifiedkfold' for pycaret.classification
and 'kfold' for pycaret.regression
. Possible values are:
kfold
for KFold CV;stratifiedkfold
for Stratified KFold CV;groupkfold
for Group KFold CV;timeseries
for TimeSeriesSplit CV; orGlobal Fold Parameter: A new parameter fold
has been added in the setup
function for pycaret.classification
and pycaret.regression
. It controls the number of folds to be used in cross validation. This is a global setting that can be over-written at function level by using fold
parameter within each function. Ignored when fold_strategy
is a custom object.
Fold Groups: Optional Group labels when fold_strategy
is groupkfold
. It takes an array with shape (n_samples, ) where n_samples is the number of rows in the training dataset. When a string is passed, it is interpreted as the column name in the dataset containing the group label.
Transformation Pipeline: All transformations are now applied after train_test_split
.
Data Type Handling: All data types handling internally has been changed from int64
and float64
to int32
and float32
respectively in order to improve memory usage and performance, as well as for better compatibility with GPU-based algorithms.
AutoML Behavior Change: automl
function in pycaret.classification
and pycaret.regression
is no more re-fitting the model on the entire dataset. As such, if the model needs to be fitted on the entire dataset including the holdout set, finalize_model
must be explicitly used.
Default Tuning Grid: Default hyperparameter tuning grid for RandomForest
, XGBoost
, CatBoost
, and LightGBM
has been amended to remove extreme values for max_depth
and other training intense parameters to speed up the tuning process.
Random Forest Default Values: Default value of n_estimators
for RandomForestClassifier
and RandomForestRegressor
has been changed from 10
to 100
to make it consistent with the default behavior of scikit-learn
.
AUC for Multiclass Classification: AUC for Multiclass target is now available in the metric evaluation.
Google Colab Display: All output printed on screen (information grid, score grids) is now format compatible with Google Colab resulting in semantic improvements.
Sampling Parameter Removed: sampling
parameter is now removed from the setup
function of pycaret.classification
and pycaret.regression
.
Type Hinting: In order to make both the usage and development easier, type hints have been added to all updated pycaret functions, in accordance with best practices. Users can leverage those by using an IDE with support for type hints.
Documentation: All Modules documentation on the website is now retired. Updated documentation is available here: https://pycaret.readthedocs.io/en/latest/
get_metrics: Returns table of available metrics used for CV.
pycaret.classification
pycaret.regression
pycaret.clustering
add_metric: Adds a custom metric for model evaluation.
pycaret.classification
pycaret.regression
pycaret.clustering
remove_metric: Remove custom metrics.
pycaret.classification
pycaret.regression
pycaret.clustering
save_config: save all global variables to a pickle file, allowing to later resume without rerunning the setup
function.
pycaret.classification
pycaret.regression
pycaret.clustering
pycaret.anomaly
load_config: Load global variables from pickle file into Python environment.
pycaret.classification
pycaret.regression
pycaret.clustering
pycaret.anomaly
pycaret.classification
pycaret.regression
pycaret.clustering
pycaret.anomaly
Following new parameters have been added:
test_data: pandas.DataFrame, default = None
If not None, test_data is used as a hold-out set, and the train_size
parameter is ignored. test_data must be labeled and the shape of data and test_data must match.
preprocess: bool, default = True
When set to False, no transformations are applied except for train_test_split
and custom transformations passed in custom_pipeline
param. Data must be ready for modeling (no missing values, no dates, categorical data encoding) when preprocess
is set to False.
imputation_type: str, default = 'simple' The type of imputation to use. Can be either 'simple' or 'iterative'.
iterative_imputation_iters: int, default = 5
The number of iterations. Ignored when imputation_type
is not 'iterative'.
categorical_iterative_imputer: str, default = 'lightgbm'
Estimator for iterative imputation of missing values in categorical features. Ignored when imputation_type
is not 'iterative'.
numeric_iterative_imputer: str, default = 'lightgbm'
Estimator for iterative imputation of missing values in numeric features. Ignored when imputation_type
is set to 'simple'.
data_split_stratify: bool or list, default = False
Controls stratification during 'train_test_split'. When set to True, will stratify by target column. To stratify on any other columns, pass a list of column names. Ignored when data_split_shuffle
is False.
fold_strategy: str or sklearn CV generator object, default = 'stratifiedkfold' / 'kfold' Choice of cross validation strategy. Possible values are:
fold: int, default = 10
The number of folds to be used in cross-validation. Must be at least 2. This is a global setting that can be over-written at the function level by using the fold
parameter. Ignored when fold_strategy
is a custom object.
fold_shuffle: bool, default = False
Controls the shuffle parameter of CV. Only applicable when fold_strategy
is 'kfold' or 'stratifiedkfold'. Ignored when fold_strategy
is a custom object.
fold_groups: str or array-like, with shape (n_samples,), default = None Optional group labels when 'GroupKFold' is used for the cross-validation. It takes an array with shape (n_samples, ) where n_samples is the number of rows in the training dataset. When a string is passed, it is interpreted as the column name in the dataset containing group labels.
use_gpu: str or bool, default = False When set to 'force', will try to use GPU with all algorithms that support it, and raise exceptions if they are unavailable. When set to True, will use GPU with algorithms that support it, and fall back to CPU if they are unavailable. When False, all algorithms are trained using CPU only.
custom_pipeline: transformer or list of transformers or tuple, default = None* When passed, will append the custom transformers in the preprocessing pipeline and are applied on each CV fold separately and on the final fit. All the custom transformations are applied after 'train_test_split' and before pycaret's internal transformations.
pycaret.classification
pycaret.regression
Following new parameters have been added:
cross_validation: bool = True
When set to False, metrics are evaluated on holdout set. fold
param is ignored when cross_validation is set to False.
errors: str = "ignore" When set to 'ignore', will skip the model with exceptions and continue. If 'raise', will stop the function when exceptions are raised.
fit_kwargs: Optional[dict] = None Dictionary of arguments passed to the fit method of the model.
groups: Optional[Union[str, Any]] = None Optional group labels when 'GroupKFold' is used for the cross-validation. It takes an array with shape (n_samples, ) where n_samples is the number of rows in the training dataset. When a string is passed, it is interpreted as the column name in the dataset containing group labels.
pycaret.classification
pycaret.regression
Following new parameters have been added:
cross_validation: bool = True
When set to False, metrics are evaluated on holdout set. fold
param is ignored when cross_validation is set to False.
fit_kwargs: Optional[dict] = None Dictionary of arguments passed to the fit method of the model.
groups: Optional[Union[str, Any]] = None Optional group labels when 'GroupKFold' is used for the cross-validation. It takes an array with shape (n_samples, ) where n_samples is the number of rows in the training dataset. When a string is passed, it is interpreted as the column name in the dataset containing group labels.
Following parameters have been removed:
ensemble_model
function directly.ensemble_model
function directly.pycaret.classification
pycaret.regression
Following new parameters have been added:
search_library: str, default = 'scikit-learn' The search library used for tuning hyperparameters. Possible values:
'scikit-learn' - default, requires no further installation https://github.com/scikit-learn/scikit-learn
'scikit-optimize' - pip install scikit-optimize
https://scikit-optimize.github.io/stable/
'tune-sklearn' - pip install tune-sklearn ray[tune]
https://github.com/ray-project/tune-sklearn
'optuna' - pip install optuna
https://optuna.org/
search_algorithm: str, default = None
The search algorithm depends on the search_library
parameter. Some search algorithms require additional libraries to be installed. When None, will use the search library-specific default algorithm.
scikit-learn
possible values:
- random (default)
- grid
scikit-optimize
possible values:
- bayesian (default)
tune-sklearn
possible values:
- random (default)
- grid
- bayesian pip install scikit-optimize
- hyperopt pip install hyperopt
- bohb pip install hpbandster ConfigSpace
optuna
possible values:
- tpe (default)
- random
early_stopping: bool or str or object, default = False
Use early stopping to stop fitting to a hyperparameter configuration if it performs poorly. Ignored when search_library
is scikit-learn, or if the estimator does not have 'partial_fit' attribute. If False or None, early stopping will not be used. Can be either an object accepted by the search library or one of the following:
early_stopping_max_iters: int, default = 10
The maximum number of epochs to run for each sampled configuration. Ignored if early_stopping
is False or None.
fit_kwargs: Optional[dict] = None Dictionary of arguments passed to the fit method of the model.
groups: Optional[Union[str, Any]] = None Optional group labels when 'GroupKFold' is used for the cross-validation. It takes an array with shape (n_samples, ) where n_samples is the number of rows in the training dataset. When a string is passed, it is interpreted as the column name in the dataset containing group labels.
return_tuner: bool, default = False When set to True, will return a tuple of (model, tuner_object).
tuner_verbose: bool or in, default = True
If True or above 0, will print messages from the tuner. Higher values print more messages. Ignored when verbose
param is False.
pycaret.classification
pycaret.regression
Following new parameters have been added:
fit_kwargs: Optional[dict] = None Dictionary of arguments passed to the fit method of the model.
groups: Optional[Union[str, Any]] = None Optional group labels when 'GroupKFold' is used for the cross-validation. It takes an array with shape (n_samples, ) where n_samples is the number of rows in the training dataset. When a string is passed, it is interpreted as the column name in the dataset containing group labels.
pycaret.classification
pycaret.regression
Following new parameters have been added:
fit_kwargs: Optional[dict] = None Dictionary of arguments passed to the fit method of the model.
groups: Optional[Union[str, Any]] = None Optional group labels when 'GroupKFold' is used for the cross-validation. It takes an array with shape (n_samples, ) where n_samples is the number of rows in the training dataset. When a string is passed, it is interpreted as the column name in the dataset containing group labels.
weights: list, default = None Sequence of weights (float or int) to weight the occurrences of predicted class labels (hard voting) or class probabilities before averaging (soft voting). Uses uniform weights when None.
The default value for the method
parameter has been changed from hard
to auto
.
pycaret.classification
pycaret.regression
Following new parameters have been added:
fit_kwargs: Optional[dict] = None Dictionary of arguments passed to the fit method of the model.
groups: Optional[Union[str, Any]] = None Optional group labels when 'GroupKFold' is used for the cross-validation. It takes an array with shape (n_samples, ) where n_samples is the number of rows in the training dataset. When a string is passed, it is interpreted as the column name in the dataset containing group labels.
pycaret.classification
Following new parameters have been added:
fit_kwargs: Optional[dict] = None Dictionary of arguments passed to the fit method of the model.
groups: Optional[Union[str, Any]] = None Optional group labels when 'GroupKFold' is used for the cross-validation. It takes an array with shape (n_samples, ) where n_samples is the number of rows in the training dataset. When a string is passed, it is interpreted as the column name in the dataset containing group labels.
pycaret.classification
pycaret.regression
Following new parameters have been added:
fold: int or scikit-learn compatible CV generator, default = None
Controls cross-validation. If None, the CV generator in the fold_strategy
parameter of the setup
function is used. When an integer is passed, it is interpreted as the 'n_splits' parameter of the CV generator in the setup
function.
fit_kwargs: Optional[dict] = None Dictionary of arguments passed to the fit method of the model.
groups: Optional[Union[str, Any]] = None Optional group labels when 'GroupKFold' is used for the cross-validation. It takes an array with shape (n_samples, ) where n_samples is the number of rows in the training dataset. When a string is passed, it is interpreted as the column name in the dataset containing group labels.
pycaret.classification
pycaret.regression
Following new parameters have been added:
fold: int or scikit-learn compatible CV generator, default = None
Controls cross-validation. If None, the CV generator in the fold_strategy
parameter of the setup
function is used. When an integer is passed, it is interpreted as the 'n_splits' parameter of the CV generator in the setup
function.
fit_kwargs: Optional[dict] = None Dictionary of arguments passed to the fit method of the model.
groups: Optional[Union[str, Any]] = None Optional group labels when 'GroupKFold' is used for the cross-validation. It takes an array with shape (n_samples, ) where n_samples is the number of rows in the training dataset. When a string is passed, it is interpreted as the column name in the dataset containing group labels.
pycaret.classification
pycaret.regression
Following new parameters have been added:
fit_kwargs: Optional[dict] = None Dictionary of arguments passed to the fit method of the model.
groups: Optional[Union[str, Any]] = None Optional group labels when 'GroupKFold' is used for the cross-validation. It takes an array with shape (n_samples, ) where n_samples is the number of rows in the training dataset. When a string is passed, it is interpreted as the column name in the dataset containing group labels.
model_only: bool, default = True When set to False, only the model object is re-trained and all the transformations in Pipeline are ignored.
pycaret.classification
pycaret.regression
pycaret.clustering
pycaret.anomaly
Following new parameters have been added:
internal: bool, default = False When True, will return extra columns and rows used internally.
raise_errors: bool, default = True
When False, will suppress all exceptions, ignoring models that couldn't be created.
2.1
a bug has been reported preventing the predict_model
function to work in the regression
module in a new notebook session when transform_target
was set to False
during model training. This issue has been fixed in PyCaret release 2.1.2
. To learn more about the issue: https://github.com/pycaret/pycaret/issues/525
2.1
a bug has been identified in MLFlow back-end. The error is only caused when log_experiment
in the setup
function is set to True and is applicable to all the modules. The cause of the error has been identified and an issue is opened with MLFlow
. The error is caused by infer_signature
function in mlflow.sklearn.log_model
and is only raised when there are missing values in the dataset. This issue has been fixed in PyCaret release 2.1.1
by skipping the signature in cases where MLFlow
raises exception.gcp
and azure
has been added in deploy_model
function for all modules. See documentation
for details.budget_time
added in compare_models
function. To set the upper limit on compare_models
training time, budget_time
parameter can be used.boruta
has been added for feature selection. By default, feature_selection_method
parameter in the setup
function is set to classic
but can be set to boruta
for feature selection using boruta algorithm. This change is applicable for pycaret.classification
and pycaret.regression
.zero
has been added in the numeric_imputation
in the setup
function. When method is set to zero
, missing values are replaced with constant 0. Default behavior of numeric_imputation
is unchanged.scale
has been added in plot_model
for all modules to enable high quality images for research publications.custom_scorer
for optimizing user defined loss function in tune_model
for pycaret.classification
and pycaret.regression
. You must use make_scorer
from sklearn
to create custom loss function that can be passed into custom_scorer
for the tune_model
function.save_model
the model
object is appended into Pipeline
, as such the behavior of Pipeline
and predict_model
is now changed. Instead of saving a list
, save_model
now saves Pipeline
object where trained model is on last position. The user functionality on front-end for predict_model
remains same.blacklist
and whitelist
is now renamed to exclude
and include
with no change in functionality.Label
column returned by predict_model
function in pycaret.classification
now returns the original label instead of encoded value. This change is made to make output from predict_model
more human-readable. A new parameter encoded_labels
is added, which is False
by default. When set to True
, it will return encoded labels.log_experiment
is set to True
is now changed. Instead of using internal save_model
functionality, it now adopts to mlflow.sklearn.save_model
to allow the use of Model Registry and MLFlow
native deployment functionalities.CatBoostClassifier
is now compatible with blend_models
in pycaret.classification
. As such blend_models
without any estimator_list
will now result in blending total of 15
estimators including CatBoostClassifier
.stack_models
in pycaret.classification
and pycaret.regression
now adopts to StackingClassifier()
and StackingRegressor
from sklearn
. As such the stack_models
function now returns sklearn
object instead of custom list
in previous versions.create_stacknet
in pycaret.classification
and pycaret.regression
is now removed.tune_model
in pycaret.classification
and pycaret.regression
now inherits params from the input estimator
. As such if you have trained xgboost
, lightgbm
or catboost
on gpu will not inherits training method from estimator
.**kwargs
argument now added in interpret_model
.pandas.Categorical
object. Internally they are converted into object and are treated as the same way as object
or bool
is treated.setup
function for pycaret.classification
and pycaret.regression
. In 2.1
it was added to prepare for the backend work required to make this change in future releases. As such using use_gpu
param in 2.1
has no impact.major
releases 0.X. For all minor monthly releases, documentation will be available on: https://pycaret.readthedocs.io/en/latest/
travis-ci
to github-actions
. pycaret-nightly
is now being published every 24 hours automatically.pycaret==2.0
. https://github.com/pycaret/pycaret/tree/master/tutorials
/pycaret/resources/
https://github.com/pycaret/pycaret/tree/master/resources
/pycaret/examples/
https://github.com/pycaret/pycaret/tree/master/examples
log_experiment
experiment_name
log_profile
log_data
added in setup
. Available in pycaret.classification
pycaret.regression
pycaret.clustering
pycaret.anomaly
pycaret.nlp
save_experiment
and load_experiment
function from pycaret.classification
pycaret.regression
pycaret.clustering
pycaret.anomaly
pycaret.nlp
is removed in PyCaret 2.0setup
is executed. logs.log
file is saved in current working directory. Function get_system_logs
can be used to access log file in notebook. html
parameter in setup
must be set to False. fix_imbalance
and fix_imbalance_method
parameter added in setup
for pycaret.classification
. When set to True, SMOTE is applied by default to create synthetic datapoints for minority class. To change the method pass any class from imblearn
that supports fit_resample
method in fix_imbalance_method
parameter. save
parameter added in plot_model
. When set to True, it saves the plot as png
or html
in current working directory. kwargs**
added in create_model
for pycaret.classification
pycaret.regression
pycaret.clustering
pycaret.anomaly
choose_better
and optimize
parameter added in tune_model
ensemble_model
blend_models
stack_models
create_stacknet
in pycaret.classification
and pycaret.regression
. Read the details below to learn more about thi added in create_model
for pycaret.classification
pycaret.regression
pycaret.clustering
pycaret.anomaly
TT (Sec)
added in compare_models
function for pycaret.classification
and pycaret.regression
MCC
metric added in score grid for pycaret.classification
automl
added in pycaret.classification
pycaret.regression
pull
added in pycaret.classification
pycaret.regression
models
added in pycaret.classification
pycaret.regression
pycaret.clustering
pycaret.anomaly
pycaret.nlp
get_logs
added in pycaret.classification
pycaret.regression
pycaret.clustering
pycaret.anomaly
pycaret.nlp
get_config
added in pycaret.classification
pycaret.regression
pycaret.clustering
pycaret.anomaly
pycaret.nlp
set_config
added in pycaret.classification
pycaret.regression
pycaret.clustering
pycaret.anomaly
pycaret.nlp
get_logs
added in pycaret.classification
pycaret.regression
pycaret.clustering
pycaret.anomaly
pycaret.nlp
compare_models
now returns top_n models defined by n_select
parameter, by default set to 1. tune_model
function in pycaret.classification
and pycaret.regression
now requires trained model object to be passed as estimator
instead of string abbreviation / ID. awscli
and shap
removed from requirements.txt. To use interpret_model
function in pycaret.classification
pycaret.regression
and deploy_model
function in pycaret.classification
pycaret.regression
pycaret.clustering
pycaret.anomaly
, these libraries will have to be installed separately. pycaret.classification
pycaret.regression
pycaret.clustering
pycaret.anomaly
pycaret.nlp
remove_perfect_collinearity
parameter added in setup()
. Default set to False. fix_imbalance
parameter added in setup()
. Default set to False. fix_imbalance_method
parameter added in setup()
. Default set to None. data_split_shuffle
parameter added in setup()
. Default set to True. folds_shuffle
parameter added in setup()
. Default set to False. n_jobs
parameter added in setup()
. Default set to -1. html
parameter added in setup()
. Default set to True. log_experiment
parameter added in setup()
. Default set to False. experiment_name
parameter added in setup()
. Default set to None. log_plots
parameter added in setup()
. Default set to False. log_profile
parameter added in setup()
. Default set to False. log_data
parameter added in setup()
. Default set to False. verbose
parameter added in setup()
. Default set to True. pycaret.classification
pycaret.regression
whitelist
parameter added in compare_models
. Default set to None. n_select
parameter added in compare_models
. Default set to 1. verbose
parameter added in compare_models
. Default set to True. pycaret.classification
pycaret.regression
pycaret.clustering
pycaret.anomaly
cross_validation
parameter added in create_model
. Default set to True. pycaret.classification
and pycaret.regression
system
parameter added in create_model
. Default set to True. ground_truth
parameter added in create_model
. Default set to None. pycaret.clustering
kwargs
parameter added in create_model
. pycaret.classification
pycaret.regression
pycaret.clustering
pycaret.anomaly
pycaret.nlp
custom_grid
parameter added in tune_model
. Default set to None. pycaret.clustering
pycaret.anomaly
pycaret.nlp
, custom_grid param must be a list of values to iterate over. choose_better
parameter added in tune_model
. Default set to False. pycaret.classification
pycaret.regression
choose_better
parameter added in ensemble_model
. Default set to False. optimize
parameter added in ensemble_model
. Default set to Accuracy
for pycaret.classification
and R2
for pycaret.regression
. pycaret.classification
are 'Accuracy', 'AUC', 'Recall', 'Precision', 'F1', 'Kappa', 'MCC' and for pycaret.regression
are 'MAE', 'MSE', 'RMSE' 'R2', 'RMSLE' and 'MAPE'.pycaret.classification
pycaret.regression
choose_better
parameter added in blend_models
. Default set to False. optimize
parameter added in blend_models
. Default set to Accuracy
for pycaret.classification
and R2
for pycaret.regression
. pycaret.classification
are 'Accuracy', 'AUC', 'Recall', 'Precision', 'F1', 'Kappa', 'MCC' and for pycaret.regression
are 'MAE', 'MSE', 'RMSE' 'R2', 'RMSLE' and 'MAPE'.pycaret.classification
pycaret.regression
choose_better
parameter added in stack_models
. Default set to False. optimize
parameter added in stack_models
. Default set to Accuracy
for pycaret.classification
and R2
for pycaret.regression
. pycaret.classification
are 'Accuracy', 'AUC', 'Recall', 'Precision', 'F1', 'Kappa', 'MCC' and for pycaret.regression
are 'MAE', 'MSE', 'RMSE' 'R2', 'RMSLE' and 'MAPE'.pycaret.classification
pycaret.regression
choose_better
parameter added in create_stacknet
. Default set to False. optimize
parameter added in create_stacknet
. Default set to Accuracy
for pycaret.classification
and R2
for pycaret.regression
. pycaret.classification
are 'Accuracy', 'AUC', 'Recall', 'Precision', 'F1', 'Kappa', 'MCC' and for pycaret.regression
are 'MAE', 'MSE', 'RMSE' 'R2', 'RMSLE' and 'MAPE'.pycaret.classification
pycaret.regression
verbose
parameter added in predict_model
. Default set to True. pycaret.classification
pycaret.regression
pycaret.clustering
pycaret.anomaly
pycaret.nlp
save
parameter added in plot_model
. Default set to False. verbose
parameter added in plot_model
. Default set to True. system
parameter added in plot_model
. Default set to True. pycaret.classification
pycaret.regression
optimize
string, default = 'Accuracy' for pycaret.classification
and 'R2' for pycaret.regression
pycaret.classification
and 'MAE', 'MSE', 'RMSE', 'R2', 'RMSLE', and 'MAPE' for pycaret.regression
use_holdout
bool, default = False pycaret.classification
pycaret.regression
pycaret.classification
pycaret.regression
pycaret.clustering
pycaret.anomaly
pycaret.nlp
type
string, default = None type
parameter only available in pycaret.classification
and pycaret.regression
pycaret.classification
pycaret.regression
pycaret.clustering
pycaret.anomaly
pycaret.nlp
experiment_name
string, default = None
When set to None current active run is used.
save
bool, default = False
When set to True, csv file is saved in current directory.
pycaret.classification
pycaret.regression
pycaret.clustering
pycaret.anomaly
pycaret.nlp
pycaret.classification
pycaret.regression
pycaret.clustering
pycaret.anomaly
pycaret.nlp
pycaret.classification
pycaret.regression
pycaret.clustering
pycaret.anomaly
pycaret.nlp
setup
is initialized in any module.