RapidPredict

RapidPredict is a Python library that simplifies the process of fitting and evaluating multiple machine learning models from scikit-learn. It's designed to provide a quick way to test various algorithms on a given dataset and compare their performance.

Installation

To install Rapid Predict from PyPI:

pip install rapidpredict



pip install -U rapidpredict

Usage

To use Rapid Predict in a project:

import rapidpredict

Classification

Example :

from rapidpredict.supervised import *

from sklearn.model_selection import train_test_split

from sklearn.datasets import load_breast_cancer

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=.25,random_state =123)





clf = rapidclassifier(verbose= 0,ignore_warnings=True, custom_metric=None)

models , predictions = clf.fit(X_train, X_test, y_train, y_test)







|Model |Accuracy	 |Balanced Accuracy |	ROC | AUC |	Recall |	Precision | F1 Score |	5 Fold F1 |	Time  Taken |

|-------------------------------|------|------|------|------|------|------|------|------|

| QuadraticDiscriminantAnalysis | 0.99 | 0.99 | 0.99 | 0.99 | 0.99 | 0.99 | 0.96 | 0.09 |

| RandomForestClassifier        | 0.99 | 0.99 | 0.99 | 0.99 | 0.99 | 0.99 | 0.96 | 1.21 |

| LogisticRegression            | 0.99 | 0.99 | 0.99 | 0.99 | 0.99 | 0.99 | 0.98 | 0.17 |

| ExtraTreesClassifier          | 0.99 | 0.98 | 0.98 | 0.99 | 0.99 | 0.99 | 0.97 | 0.80 |

| RidgeClassifier               | 0.99 | 0.98 | 0.98 | 0.99 | 0.99 | 0.99 | 0.96 | 0.13 |

| LinearSVC                     | 0.99 | 0.98 | 0.98 | 0.99 | 0.99 | 0.99 | 0.96 | 0.10 |

| SVC                           | 0.99 | 0.98 | 0.98 | 0.99 | 0.99 | 0.99 | 0.97 | 0.10 |

| RidgeClassifierCV             | 0.99 | 0.98 | 0.98 | 0.99 | 0.99 | 0.99 | 0.96 | 0.17 |

| LabelPropagation              | 0.98 | 0.98 | 0.98 | 0.98 | 0.98 | 0.98 | 0.94 | 0.17 |

| LabelSpreading                | 0.98 | 0.98 | 0.98 | 0.98 | 0.98 | 0.98 | 0.96 | 0.19 |

| SGDClassifier                 | 0.98 | 0.98 | 0.98 | 0.98 | 0.98 | 0.98 | 0.97 | 0.09 |

| Perceptron                    | 0.98 | 0.98 | 0.98 | 0.98 | 0.98 | 0.98 | 0.97 | 0.08 |

| KNeighborsClassifier          | 0.98 | 0.98 | 0.98 | 0.98 | 0.98 | 0.98 | 0.97 | 0.11 |

| DecisionTreeClassifier        | 0.98 | 0.98 | 0.98 | 0.98 | 0.98 | 0.98 | 0.93 | 0.09 |

| BernoulliNB                   | 0.98 | 0.98 | 0.98 | 0.98 | 0.98 | 0.98 | 0.93 | 0.09 |

| LinearDiscriminantAnalysis    | 0.98 | 0.97 | 0.97 | 0.98 | 0.98 | 0.98 | 0.96 | 0.14 |

| CalibratedClassifierCV        | 0.98 | 0.97 | 0.97 | 0.98 | 0.98 | 0.98 | 0.97 | 0.24 |

| AdaBoostClassifier            | 0.97 | 0.97 | 0.97 | 0.97 | 0.97 | 0.97 | 0.95 | 0.89 |

| PassiveAggressiveClassifier   | 0.97 | 0.97 | 0.97 | 0.97 | 0.97 | 0.97 | 0.97 | 0.09 |

| XGBClassifier                 | 0.97 | 0.97 | 0.97 | 0.97 | 0.97 | 0.97 | 0.97 | 0.45 |

| BaggingClassifier             | 0.97 | 0.96 | 0.96 | 0.97 | 0.97 | 0.97 | 0.95 | 0.32 |

| NuSVC                         | 0.97 | 0.95 | 0.95 | 0.97 | 0.97 | 0.96 | 0.95 | 0.12 |

| NearestCentroid               | 0.97 | 0.95 | 0.95 | 0.97 | 0.97 | 0.96 | 0.94 | 0.08 |

| GaussianNB                    | 0.97 | 0.95 | 0.95 | 0.97 | 0.97 | 0.96 | 0.94 | 0.08 |

| ExtraTreeClassifier           | 0.94 | 0.94 | 0.94 | 0.94 | 0.94 | 0.94 | 0.93 | 0.08 |

| DummyClassifier               | 0.62 | 0.50 | 0.50 | 0.62 | 0.39 | 0.48 | 0.77 | 0.08 |

Plot Target values

plot_target(y)

Comparing models using bar graph

compareModels_bargraph(predictions["F1 Score"] ,models.index)

Comparing models using box plot

compareModels_boxplot(predictions["F1 Score"] ,models.index)

Heatmap

This code updated from github "Lazypredic-Shankar Rao Pandala"