Home - News ( United States | United Kingdom | Italy | Germany ) - Football scores

Showing content from https://pypi.python.org/pypi/lazypredict below:

lazypredict · PyPI

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

• Over 40 built-in machine learning models
• Automatic model selection for classification and regression
• Built-in MLflow integration for experiment tracking
• Support for Python 3.8 through 3.13
• Custom metric evaluation support

To install Lazy Predict:

pip install lazypredict

To use Lazy Predict in a project:

import lazypredict

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)

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)

Lazy Predict includes built-in MLflow integration. Enable it by setting the MLflow tracking URI:

import os
os.environ['MLFLOW_TRACKING_URI'] = 'sqlite:///mlflow.db'

# MLflow tracking will be automatically enabled
reg = LazyRegressor(verbose=0, ignore_warnings=True)
models, predictions = reg.fit(X_train, X_test, y_train, y_test)

Automatically tracks:

• Model metrics (R-squared, RMSE, etc.)
• Training time
• Model parameters
• Model artifacts

• Added MLflow integration for experiment tracking
• Added support for Python 3.13
• Updated all dependencies to latest versions
• Improved model logging and tracking capabilities
• Added automatic model signature logging with MLflow

• Updated the default version to 3.9

• Fixed issue with older version of Scikit-learn
• Reduced dependencies sctrictly to few

• 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.

• Removed catboost regressor and classifier

• Added xgboost, lightgbm, catboost regressors and classifiers

• Removed troublesome regressors from list of CLASSIFIERS

• 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

• Removed TheilSenRegressor from list of REGRESSORS
• Removed GaussianProcessClassifier from list of CLASSIFIERS

• Fixed automatic deployment issue.

• Release of Regression feature.

• Release of Classification feature.

• First release on PyPI.

RetroSearch is an open source project built by @garambo | Open a GitHub Issue

Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo

HTML: 3.2 | Encoding: UTF-8 | Version: 0.7.4