Lazy Predict

Lazy Predict helps build a lot of basic models without much code and helps understand which models work better without any parameter tuning.

• Free software: MIT license
• Documentation: https://lazypredict.readthedocs.io

Features

• Over 40 built-in machine learning models
• Automatic model selection for classification, regression, and time series forecasting
• 20+ forecasting models: statistical (ETS, ARIMA, Theta), ML (Random Forest, XGBoost, etc.), deep learning (LSTM, GRU), and pretrained foundation models (TimesFM)
• Automatic seasonal period detection via ACF
• Multiple categorical encoding strategies (OneHot, Ordinal, Target, Binary)
• Built-in MLflow integration for experiment tracking
• GPU acceleration: XGBoost, LightGBM, CatBoost, cuML (RAPIDS), LSTM/GRU, TimesFM
• Support for Python 3.9 through 3.13
• Custom metric evaluation support
• Configurable timeout and cross-validation
• Intel Extension for Scikit-learn acceleration support

Installation

pip (PyPI)

pip install lazypredict

conda (conda-forge)

conda install -c conda-forge lazypredict

Optional extras (pip only)

Install with boosting libraries (XGBoost, LightGBM, CatBoost):

pip install lazypredict[boost]

Install with time series forecasting support:

pip install lazypredict[timeseries]          # statsmodels + pmdarima
pip install lazypredict[timeseries,deeplearning]  # + LSTM/GRU via PyTorch
pip install lazypredict[timeseries,foundation]    # + Google TimesFM (Python 3.10-3.11)

Install with all optional dependencies:

pip install lazypredict[all]

Usage

To use Lazy Predict in a project:

import lazypredict

Classification

Example:

from lazypredict.Supervised 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=0.5, 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)

Advanced Options

# With categorical encoding, timeout, cross-validation, and GPU
clf = LazyClassifier(
    verbose=1,                          # Show progress
    ignore_warnings=True,               # Suppress warnings
    custom_metric=None,                 # Use default metrics
    predictions=True,                   # Return predictions
    classifiers='all',                  # Use all available classifiers
    categorical_encoder='onehot',       # Encoding: 'onehot', 'ordinal', 'target', 'binary'
    timeout=60,                         # Max time per model in seconds
    cv=5,                               # Cross-validation folds (optional)
    use_gpu=True                        # Enable GPU acceleration
)
models, predictions = clf.fit(X_train, X_test, y_train, y_test)

Parameters:

• verbose (int): 0 for silent, 1 for progress display
• ignore_warnings (bool): Suppress scikit-learn warnings
• custom_metric (callable): Custom evaluation metric
• predictions (bool): Return prediction DataFrame
• classifiers (str/list): 'all' or list of classifier names
• categorical_encoder (str): Encoding strategy for categorical features
  • 'onehot': One-hot encoding (default)
  • 'ordinal': Ordinal encoding
  • 'target': Target encoding (requires category-encoders)
  • 'binary': Binary encoding (requires category-encoders)
• timeout (int): Maximum seconds per model (None for no limit)
• cv (int): Number of cross-validation folds (None to disable)
• use_gpu (bool): Enable GPU acceleration for supported models (default False)

| 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

Example:

from lazypredict.Supervised import LazyRegressor
from sklearn import datasets
from sklearn.utils import shuffle
import numpy as np

diabetes  = datasets.load_diabetes()
X, y = shuffle(diabetes.data, diabetes.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)

Advanced Options

# With categorical encoding, timeout, and GPU
reg = LazyRegressor(
    verbose=1,                          # Show progress
    ignore_warnings=True,               # Suppress warnings
    custom_metric=None,                 # Use default metrics
    predictions=True,                   # Return predictions
    regressors='all',                   # Use all available regressors
    categorical_encoder='ordinal',      # Encoding: 'onehot', 'ordinal', 'target', 'binary'
    timeout=120,                        # Max time per model in seconds
    use_gpu=True                        # Enable GPU acceleration
)
models, predictions = reg.fit(X_train, X_test, y_train, y_test)

Parameters:

• verbose (int): 0 for silent, 1 for progress display
• ignore_warnings (bool): Suppress scikit-learn warnings
• custom_metric (callable): Custom evaluation metric
• predictions (bool): Return prediction DataFrame
• regressors (str/list): 'all' or list of regressor names
• categorical_encoder (str): Encoding strategy for categorical features
  • 'onehot': One-hot encoding (default)
  • 'ordinal': Ordinal encoding
  • 'target': Target encoding (requires category-encoders)