# 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"](https://github.com/shankarpandala/lazypredict)
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"description": "\n# RapidPredict\n\nRapidPredict 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. \n\n\n\n\n\n# Installation\n\n\n\nTo install Rapid Predict from PyPI:\n\n\n\n pip install rapidpredict\n\n\n\n pip install -U rapidpredict\n\n\n\n\n\n \n\n\n\n# Usage\n\n\n\nTo use Rapid Predict in a project:\n\n\n\n import rapidpredict\n\n\n\n\n\n\n\n## Classification\n\n\n\nExample :\n\n\n\n from rapidpredict.supervised import *\n\n from sklearn.model_selection import train_test_split\n\n from sklearn.datasets import load_breast_cancer\n\n data = load_breast_cancer()\n\n X = data.data\n\n y= data.target\n\n\n\n X_train, X_test, y_train, y_test = train_test_split(X, y,test_size=.25,random_state =123)\n\n\n\n\n\n clf = rapidclassifier(verbose= 0,ignore_warnings=True, custom_metric=None)\n\n models , predictions = clf.fit(X_train, X_test, y_train, y_test)\n\n\n\n\n\n\n\n |Model |Accuracy\t |Balanced Accuracy |\tROC | AUC |\tRecall |\tPrecision | F1 Score |\t5 Fold F1 |\tTime Taken |\n\n |-------------------------------|------|------|------|------|------|------|------|------|\n\n | QuadraticDiscriminantAnalysis | 0.99 | 0.99 | 0.99 | 0.99 | 0.99 | 0.99 | 0.96 | 0.09 |\n\n | RandomForestClassifier | 0.99 | 0.99 | 0.99 | 0.99 | 0.99 | 0.99 | 0.96 | 1.21 |\n\n | LogisticRegression | 0.99 | 0.99 | 0.99 | 0.99 | 0.99 | 0.99 | 0.98 | 0.17 |\n\n | ExtraTreesClassifier | 0.99 | 0.98 | 0.98 | 0.99 | 0.99 | 0.99 | 0.97 | 0.80 |\n\n | RidgeClassifier | 0.99 | 0.98 | 0.98 | 0.99 | 0.99 | 0.99 | 0.96 | 0.13 |\n\n | LinearSVC | 0.99 | 0.98 | 0.98 | 0.99 | 0.99 | 0.99 | 0.96 | 0.10 |\n\n | SVC | 0.99 | 0.98 | 0.98 | 0.99 | 0.99 | 0.99 | 0.97 | 0.10 |\n\n | RidgeClassifierCV | 0.99 | 0.98 | 0.98 | 0.99 | 0.99 | 0.99 | 0.96 | 0.17 |\n\n | LabelPropagation | 0.98 | 0.98 | 0.98 | 0.98 | 0.98 | 0.98 | 0.94 | 0.17 |\n\n | LabelSpreading | 0.98 | 0.98 | 0.98 | 0.98 | 0.98 | 0.98 | 0.96 | 0.19 |\n\n | SGDClassifier | 0.98 | 0.98 | 0.98 | 0.98 | 0.98 | 0.98 | 0.97 | 0.09 |\n\n | Perceptron | 0.98 | 0.98 | 0.98 | 0.98 | 0.98 | 0.98 | 0.97 | 0.08 |\n\n | KNeighborsClassifier | 0.98 | 0.98 | 0.98 | 0.98 | 0.98 | 0.98 | 0.97 | 0.11 |\n\n | DecisionTreeClassifier | 0.98 | 0.98 | 0.98 | 0.98 | 0.98 | 0.98 | 0.93 | 0.09 |\n\n | BernoulliNB | 0.98 | 0.98 | 0.98 | 0.98 | 0.98 | 0.98 | 0.93 | 0.09 |\n\n | LinearDiscriminantAnalysis | 0.98 | 0.97 | 0.97 | 0.98 | 0.98 | 0.98 | 0.96 | 0.14 |\n\n | CalibratedClassifierCV | 0.98 | 0.97 | 0.97 | 0.98 | 0.98 | 0.98 | 0.97 | 0.24 |\n\n | AdaBoostClassifier | 0.97 | 0.97 | 0.97 | 0.97 | 0.97 | 0.97 | 0.95 | 0.89 |\n\n | PassiveAggressiveClassifier | 0.97 | 0.97 | 0.97 | 0.97 | 0.97 | 0.97 | 0.97 | 0.09 |\n\n | XGBClassifier | 0.97 | 0.97 | 0.97 | 0.97 | 0.97 | 0.97 | 0.97 | 0.45 |\n\n | BaggingClassifier | 0.97 | 0.96 | 0.96 | 0.97 | 0.97 | 0.97 | 0.95 | 0.32 |\n\n | NuSVC | 0.97 | 0.95 | 0.95 | 0.97 | 0.97 | 0.96 | 0.95 | 0.12 |\n\n | NearestCentroid | 0.97 | 0.95 | 0.95 | 0.97 | 0.97 | 0.96 | 0.94 | 0.08 |\n\n | GaussianNB | 0.97 | 0.95 | 0.95 | 0.97 | 0.97 | 0.96 | 0.94 | 0.08 |\n\n | ExtraTreeClassifier | 0.94 | 0.94 | 0.94 | 0.94 | 0.94 | 0.94 | 0.93 | 0.08 |\n\n | DummyClassifier | 0.62 | 0.50 | 0.50 | 0.62 | 0.39 | 0.48 | 0.77 | 0.08 |\n\n\n\n\n\n\n\n## Plot Target values\n\n\n\n plot_target(y)\n\n\n\n\n\n\n\n\n\n\n\n## Comparing models using bar graph\n\n \n\n compareModels_bargraph(predictions[\"F1 Score\"] ,models.index)\n\n \n\n\n\n \n\n\n\n\n\n## Comparing models using box plot\n\n \n\n compareModels_boxplot(predictions[\"F1 Score\"] ,models.index)\n\n\n\n\n\n\n\n\n\n\n\n## Heatmap\n\n\n\n \n\n\n\n\n\n\n\nThis code updated from github [\"Lazypredic-Shankar Rao Pandala\"](https://github.com/shankarpandala/lazypredict) \n\n",
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