all-predict 0.1.0

An enhanced LazyPredict-like library with more models, hyperparameter tuning, and visualizations.

README.md

all_predict

Author: Santu Chall
Email: [e-mail]

Overview

all_predict is an advanced, production-ready alternative to LazyPredict, providing:

• 30+ regression models and 30+ classification models from scikit-learn, XGBoost, LightGBM, CatBoost, and more.
• Automatic evaluation and ranking of all models.
• Top-3 model hyperparameter tuning using GridSearchCV with robust, extended parameter grids for deeper optimization.
• Performance metrics, training/prediction time logging.
• Model saving/loading utilities.
• Comparison visualizations.

Installation

pip install all_predict

Quick Start

Regression Example

from sklearn.datasets import make_regression
from all_predict.regression import LazyRegressorPlus

X, y = make_regression(n_samples=500, n_features=10, noise=0.1, random_state=42)

reg = LazyRegressorPlus(verbose=True)
results, tuned = reg.fit(X, y)
print(results.head())
print(tuned)

Classification Example

from sklearn.datasets import make_classification
from all_predict.classification import LazyClassifierPlus

X, y = make_classification(n_samples=500, n_features=10, n_classes=2, random_state=42)

clf = LazyClassifierPlus(verbose=True)
results, tuned = clf.fit(X, y)
print(results.head())
print(tuned)

Robust Parameter Grids

The GridSearchCV now uses expanded hyperparameter grids tailored for each model type:

• Tree-based models: n_estimators, max_depth, min_samples_split, min_samples_leaf, max_features, bootstrap, learning rate (if applicable)
• Linear models: alpha, l1_ratio, regularization type, solver variations
• Boosting models: n_estimators, learning_rate, max_depth, colsample_bytree, subsample, regularization terms
• SVM: C, kernel, gamma, degree
• KNN: n_neighbors, weights, metric

These grids allow the tuner to explore broader, more robust parameter spaces for significantly better model performance.

Features

• Extended Model List: More models than LazyPredict.
• Top-3 GridSearch Tuning: Automated hyperparameter optimization with deep parameter grids.
• Persistence: Save and load best models.
• Visualization: Compare performance and timings.

Output Example

Regression

Model	R2	RMSE	MAE	Train Time	Predict Time
RandomForestRegressor	0.95	2.10	1.50	0.12	0.02

Tuned Models

Model	Best Params	Best CV Score	Test Score
RandomForestRegressor	{"n_estimators":200, "max_depth":10}	0.96	0.95

Comparison with LazyPredict

Feature	LazyPredict	all_predict
Model Count	~20	60+
Auto GridSearch Tuning	❌	✅
Robust Parameter Grids	❌	✅
Model Saving/Loading	❌	✅
Visualization	❌	✅
Parallel Processing	Limited	Full

License

MIT License.