[Updated] Lazy Predict help build a lot of basic models without much code and helps understand which models works better without any parameter tuning
Project description
Lazy Predict [Nightly]
Lazy Predict 2.0 to help you benchmark models without much code and understand what works better without any hyperparameter tuning.
Coming soon
- LLM Benchmarking - Foundational
- LLM Benchmarking - Task Specific
- Text Classification
- Token Classification
- Text Summarization
- Text Similarity
- Statistical Model Benchmarking
Getting started
To install Lazy Predict Nightly:
pip install lazypredict-nightly
To use Lazy Predict in a project:
import lazypredict
Classification
from lazypredict import LazyClassifier
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
data = load_breast_cancer()
X = data.data
y= data.target
X_train, X_test, y_train, y_test = train_test_split(X, y,test_size=.5,random_state =123)
clf = LazyClassifier(verbose=0,ignore_warnings=True, custom_metric=None)
models,predictions = clf.fit(X_train, X_test, y_train, y_test)
print(models)
| Model | Accuracy | Balanced Accuracy | ROC AUC | F1 Score | Time Taken |
|:-------------------------------|-----------:|--------------------:|----------:|-----------:|-------------:|
| LinearSVC | 0.989474 | 0.987544 | 0.987544 | 0.989462 | 0.0150008 |
| SGDClassifier | 0.989474 | 0.987544 | 0.987544 | 0.989462 | 0.0109992 |
| MLPClassifier | 0.985965 | 0.986904 | 0.986904 | 0.985994 | 0.426 |
| Perceptron | 0.985965 | 0.984797 | 0.984797 | 0.985965 | 0.0120046 |
| LogisticRegression | 0.985965 | 0.98269 | 0.98269 | 0.985934 | 0.0200036 |
| LogisticRegressionCV | 0.985965 | 0.98269 | 0.98269 | 0.985934 | 0.262997 |
| SVC | 0.982456 | 0.979942 | 0.979942 | 0.982437 | 0.0140011 |
| CalibratedClassifierCV | 0.982456 | 0.975728 | 0.975728 | 0.982357 | 0.0350015 |
| PassiveAggressiveClassifier | 0.975439 | 0.974448 | 0.974448 | 0.975464 | 0.0130005 |
| LabelPropagation | 0.975439 | 0.974448 | 0.974448 | 0.975464 | 0.0429988 |
| LabelSpreading | 0.975439 | 0.974448 | 0.974448 | 0.975464 | 0.0310006 |
| RandomForestClassifier | 0.97193 | 0.969594 | 0.969594 | 0.97193 | 0.033 |
| GradientBoostingClassifier | 0.97193 | 0.967486 | 0.967486 | 0.971869 | 0.166998 |
| QuadraticDiscriminantAnalysis | 0.964912 | 0.966206 | 0.966206 | 0.965052 | 0.0119994 |
| HistGradientBoostingClassifier | 0.968421 | 0.964739 | 0.964739 | 0.968387 | 0.682003 |
| RidgeClassifierCV | 0.97193 | 0.963272 | 0.963272 | 0.971736 | 0.0130029 |
| RidgeClassifier | 0.968421 | 0.960525 | 0.960525 | 0.968242 | 0.0119977 |
| AdaBoostClassifier | 0.961404 | 0.959245 | 0.959245 | 0.961444 | 0.204998 |
| ExtraTreesClassifier | 0.961404 | 0.957138 | 0.957138 | 0.961362 | 0.0270066 |
| KNeighborsClassifier | 0.961404 | 0.95503 | 0.95503 | 0.961276 | 0.0560005 |
| BaggingClassifier | 0.947368 | 0.954577 | 0.954577 | 0.947882 | 0.0559971 |
| BernoulliNB | 0.950877 | 0.951003 | 0.951003 | 0.951072 | 0.0169988 |
| LinearDiscriminantAnalysis | 0.961404 | 0.950816 | 0.950816 | 0.961089 | 0.0199995 |
| GaussianNB | 0.954386 | 0.949536 | 0.949536 | 0.954337 | 0.0139935 |
| NuSVC | 0.954386 | 0.943215 | 0.943215 | 0.954014 | 0.019989 |
| DecisionTreeClassifier | 0.936842 | 0.933693 | 0.933693 | 0.936971 | 0.0170023 |
| NearestCentroid | 0.947368 | 0.933506 | 0.933506 | 0.946801 | 0.0160074 |
| ExtraTreeClassifier | 0.922807 | 0.912168 | 0.912168 | 0.922462 | 0.0109999 |
| CheckingClassifier | 0.361404 | 0.5 | 0.5 | 0.191879 | 0.0170043 |
| DummyClassifier | 0.512281 | 0.489598 | 0.489598 | 0.518924 | 0.0119965 |
Regression
from lazypredict import LazyRegressor
from sklearn.datasets import fetch_california_housing
from sklearn.utils import shuffle
import numpy as np
housing = fetch_california_housing()
X, y = shuffle(housing.data, housing.target, random_state=13)
X = X.astype(np.float32)
offset = int(X.shape[0] * 0.9)
X_train, y_train = X[:offset], y[:offset]
X_test, y_test = X[offset:], y[offset:]
reg = LazyRegressor(verbose=0, ignore_warnings=False, custom_metric=None)
models, predictions = reg.fit(X_train, X_test, y_train, y_test)
print(models)
# LightGBM may print informational warnings while fitting; this is expected.
| Model | Adjusted R-Squared | R-Squared | RMSE | Time Taken |
|:------------------------------|-------------------:|----------:|------:|-----------:|
| SVR | 0.83 | 0.88 | 2.62 | 0.01 |
| BaggingRegressor | 0.83 | 0.88 | 2.63 | 0.03 |
| NuSVR | 0.82 | 0.86 | 2.76 | 0.03 |
| RandomForestRegressor | 0.81 | 0.86 | 2.78 | 0.21 |
| XGBRegressor | 0.81 | 0.86 | 2.79 | 0.06 |
| GradientBoostingRegressor | 0.81 | 0.86 | 2.84 | 0.11 |
| ExtraTreesRegressor | 0.79 | 0.84 | 2.98 | 0.12 |
| AdaBoostRegressor | 0.78 | 0.83 | 3.04 | 0.07 |
| HistGradientBoostingRegressor | 0.77 | 0.83 | 3.06 | 0.17 |
| PoissonRegressor | 0.77 | 0.83 | 3.11 | 0.01 |
| LGBMRegressor | 0.77 | 0.83 | 3.11 | 0.07 |
| KNeighborsRegressor | 0.77 | 0.83 | 3.12 | 0.01 |
| DecisionTreeRegressor | 0.65 | 0.74 | 3.79 | 0.01 |
| MLPRegressor | 0.65 | 0.74 | 3.80 | 1.63 |
| HuberRegressor | 0.64 | 0.74 | 3.84 | 0.01 |
| GammaRegressor | 0.64 | 0.73 | 3.88 | 0.01 |
| LinearSVR | 0.62 | 0.72 | 3.96 | 0.01 |
| RidgeCV | 0.62 | 0.72 | 3.97 | 0.01 |
| BayesianRidge | 0.62 | 0.72 | 3.97 | 0.01 |
| Ridge | 0.62 | 0.72 | 3.97 | 0.01 |
| TransformedTargetRegressor | 0.62 | 0.72 | 3.97 | 0.01 |
| LinearRegression | 0.62 | 0.72 | 3.97 | 0.01 |
| ElasticNetCV | 0.62 | 0.72 | 3.98 | 0.04 |
| LassoCV | 0.62 | 0.72 | 3.98 | 0.06 |
| LassoLarsIC | 0.62 | 0.72 | 3.98 | 0.01 |
| LassoLarsCV | 0.62 | 0.72 | 3.98 | 0.02 |
| Lars | 0.61 | 0.72 | 3.99 | 0.01 |
| LarsCV | 0.61 | 0.71 | 4.02 | 0.04 |
| SGDRegressor | 0.60 | 0.70 | 4.07 | 0.01 |
| TweedieRegressor | 0.59 | 0.70 | 4.12 | 0.01 |
| GeneralizedLinearRegressor | 0.59 | 0.70 | 4.12 | 0.01 |
| ElasticNet | 0.58 | 0.69 | 4.16 | 0.01 |
| Lasso | 0.54 | 0.66 | 4.35 | 0.02 |
| RANSACRegressor | 0.53 | 0.65 | 4.41 | 0.04 |
| OrthogonalMatchingPursuitCV | 0.45 | 0.59 | 4.78 | 0.02 |
| PassiveAggressiveRegressor | 0.37 | 0.54 | 5.09 | 0.01 |
| GaussianProcessRegressor | 0.23 | 0.43 | 5.65 | 0.03 |
| OrthogonalMatchingPursuit | 0.16 | 0.38 | 5.89 | 0.01 |
| ExtraTreeRegressor | 0.08 | 0.32 | 6.17 | 0.01 |
| DummyRegressor | -0.38 | -0.02 | 7.56 | 0.01 |
| LassoLars | -0.38 | -0.02 | 7.56 | 0.01 |
| KernelRidge | -11.50 | -8.25 | 22.74 | 0.01 |
History
0.3.2 (2024-03-25)
- Major import bug fix
- Cleanup
0.3.1 (2024-03-03)
- Minor cleanups
0.3.0 (2024-03-03)
- Fixed OneHotEncoder Bug
0.2.11 (2022-02-06)
- Updated the default version to 3.9
0.2.10 (2022-02-06)
- Fixed issue with older version of Scikit-learn
- Reduced dependencies sctrictly to few
0.2.8 (2021-02-06)
- Removed StackingRegressor and CheckingClassifier.
- Added provided_models method.
- Added adjusted r-squared metric.
- Added cardinality check to split categorical columns into low and high cardinality features.
- Added different transformation pipeline for low and high cardinality features.
- Included all number dtypes as inputs.
- Fixed dependencies.
- Improved documentation.
0.2.7 (2020-07-09)
- Removed catboost regressor and classifier
0.2.6 (2020-01-22)
- Added xgboost, lightgbm, catboost regressors and classifiers
0.2.5 (2020-01-20)
- Removed troublesome regressors from list of CLASSIFIERS
0.2.4 (2020-01-19)
- Removed troublesome regressors from list of REGRESSORS
- Added feature to input custom metric for evaluation
- Added feature to return predictions as dataframe
- Added model training time for each model
0.2.3 (2019-11-22)
- Removed TheilSenRegressor from list of REGRESSORS
- Removed GaussianProcessClassifier from list of CLASSIFIERS
0.2.2 (2019-11-18)
- Fixed automatic deployment issue.
0.2.1 (2019-11-18)
- Release of Regression feature.
0.2.0 (2019-11-17)
- Release of Classification feature.
0.1.0 (2019-11-16)
- First release on PyPI.
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