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funpredict
Introducing Fun Predict, the ultimate time-saver for machine learning! No more complex coding or tedious parameter tuning - just sit back and let Fun Predict build your basic models with ease. It's like having a personal assistant for your machine learning projects, making the process simple, efficient, and, well, Fun! 🛋
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Fun Predict🤖
Fun Predict is a free, open-source Python library that helps you build and compare machine learning models easily, without writing much code. It allows you to quickly and easily evaluate a variety of models without having to write a lot of code or tune hyperparameters.
Installation
To install Fun Predict:
pip install funpredict
Usage
To use Fun Predict in a project:
import funpredict
Classification
Example :
from funpredict.fun_model import PlayClassifier
from sklearn.datasets import load_wine
from sklearn.model_selection import train_test_split
# Test with a Classification model
data = load_wine()
X,y = data.data,data.target
X_train, X_test, y_train, y_test = train_test_split(X, y,test_size=.5,random_state =42)
clf = PlayClassifier(verbose=0,ignore_warnings=True, custom_metric=None)
models,predictions = clf.fit(X_train, X_test, y_train, y_test,'multiclass')
# If you confirm which model working best then choose hare.
model_dictionary = clf.provide_models(X_train,X_test,y_train,y_test)
print(models)
| Accuracy | Balanced Accuracy| F1 Score | Time Taken |
-----------------------------------------------------------------------------------------|
| Model : |
| -----------------------------------------------------+
| ExtraTreesClassifier | 1.00 | 1.00 | 1.00 | 0.27 |
| RandomForestClassifier | 1.00 | 1.00 | 1.00 | 0.40 |
| GaussianNB | 1.00 | 1.00 | 1.00 | 0.02 |
| CatBoostClassifier | 0.99 | 0.99 | 0.99 | 3.32 |
| KNeighborsClassifier | 0.99 | 0.99 | 0.99 | 0.03 |
| RidgeClassifierCV | 0.99 | 0.99 | 0.99 | 0.02 |
| PassiveAggressiveClassifier | 0.99 | 0.99 | 0.99 | 0.04 |
| LogisticRegression | 0.99 | 0.99 | 0.99 | 0.03 |
| NearestCentroid | 0.98 | 0.98 | 0.98 | 0.03 |
| LGBMClassifier | 0.98 | 0.98 | 0.98 | 0.15 |
| Perceptron | 0.98 | 0.98 | 0.98 | 0.04 |
| SGDClassifier | 0.98 | 0.98 | 0.98 | 0.02 |
| LinearDiscriminantAnalysis | 0.98 | 0.98 | 0.98 | 0.02 |
| LinearSVC | 0.98 | 0.98 | 0.98 | 0.02 |
| RidgeClassifier | 0.98 | 0.98 | 0.98 | 0.02 |
| NuSVC | 0.98 | 0.98 | 0.98 | 0.02 |
| SVC | 0.98 | 0.98 | 0.98 | 0.02 |
| LabelPropagation | 0.97 | 0.97 | 0.97 | 0.02 |
| LabelSpreading | 0.97 | 0.97 | 0.97 | 0.02 |
| XGBClassifier | 0.97 | 0.97 | 0.97 | 0.10 |
| BaggingClassifier | 0.97 | 0.97 | 0.97 | 0.11 |
| BernoulliNB | 0.94 | 0.94 | 0.94 | 0.04 |
| CalibratedClassifierCV | 0.94 | 0.94 | 0.94 | 0.15 |
| AdaBoostClassifier | 0.93 | 0.93 | 0.93 | 0.29 |
| QuadraticDiscriminantAnalysis | 0.93 | 0.93 | 0.93 | 0.04 |
| DecisionTreeClassifier | 0.88 | 0.88 | 0.88 | 0.04 |
| ExtraTreeClassifier | 0.83 | 0.83 | 0.83 | 0.04 |
| DummyClassifier | 0.34 | 0.33 | 0.17 | 0.03 |
-------------------------------------------------------------------------------------------
# Vertical bar plot
clf.barplot(predictions)
# Horizontal bar plot
clf.hbarplot(predictions)
Regression
Example :
from funpredict.fun_model import PlayRegressor
from sklearn.datasets import load_diabetes
from sklearn.model_selection import train_test_split
# Test with Regressor Model
data = load_diabetes()
X,y = data.data, data.target
X_train, X_test, y_train, y_test = train_test_split(X, y,test_size=.5,random_state =42)
rgs = PlayRegressor(verbose=0,ignore_warnings=True, custom_metric=None)
models,predictions = rgs.fit(X_train, X_test, y_train, y_test)
# If you confirm which model works best then choose hare.
model_dictionary = rgs.provide_models(X_train, X_test,y_train,y_test)
print(models)
|-----------------------------------------------------------------------------------------|
| Model | Adjusted R-Squared | R-Squared | RMSE | Time Taken |
|:------------------------------|-------------------:|----------:|------:|-----------:|
| BayesianRidge | 0.45 | 0.48 | 54.46 | 0.04 |
| ElasticNetCV | 0.46 | 0.48 | 54.41 | 0.31 |
| RidgeCV | 0.45 | 0.48 | 54.51 | 0.04 |
| LinearRegression | 0.45 | 0.48 | 54.58 | 0.03 |
| TransformedTargetRegressor | 0.45 | 0.48 | 54.58 | 0.04 |
| Lars | 0.45 | 0.48 | 54.58 | 0.05 |
| Ridge | 0.45 | 0.48 | 54.59 | 0.03 |
| Lasso | 0.45 | 0.47 | 54.69 | 0.03 |
| LassoLars | 0.45 | 0.47 | 54.69 | 0.03 |
| LassoCV | 0.45 | 0.47 | 54.70 | 0.28 |
| LassoLarsCV | 0.45 | 0.47 | 54.71 | 0.07 |
| PoissonRegressor | 0.45 | 0.47 | 54.76 | 0.04 |
| SGDRegressor | 0.45 | 0.47 | 54.76 | 0.04 |
| OrthogonalMatchingPursuitCV | 0.45 | 0.47 | 54.80 | 0.06 |
| HuberRegressor | 0.44 | 0.47 | 54.96 | 0.06 |
| LassoLarsIC | 0.44 | 0.47 | 55.02 | 0.03 |
| ElasticNet | 0.44 | 0.47 | 55.05 | 0.03 |
| LarsCV | 0.43 | 0.45 | 55.72 | 0.09 |
| AdaBoostRegressor | 0.42 | 0.44 | 56.34 | 0.34 |
| TweedieRegressor | 0.41 | 0.44 | 56.40 | 0.03 |
| ExtraTreesRegressor | 0.41 | 0.44 | 56.60 | 0.40 |
| PassiveAggressiveRegressor | 0.41 | 0.44 | 56.61 | 0.03 |
| GammaRegressor | 0.41 | 0.43 | 56.79 | 0.02 |
| LGBMRegressor | 0.40 | 0.43 | 57.04 | 0.12 |
| CatBoostRegressor | 0.39 | 0.42 | 57.47 | 3.26 |
| RandomForestRegressor | 0.38 | 0.41 | 58.00 | 0.79 |
| HistGradientBoostingRegressor | 0.36 | 0.39 | 58.84 | 0.27 |
| GradientBoostingRegressor | 0.36 | 0.39 | 58.95 | 0.31 |
| BaggingRegressor | 0.33 | 0.36 | 60.12 | 0.11 |
| KNeighborsRegressor | 0.29 | 0.32 | 62.09 | 0.03 |
| XGBRegressor | 0.23 | 0.27 | 64.59 | 0.21 |
| OrthogonalMatchingPursuit | 0.23 | 0.26 | 64.86 | 0.05 |
| RANSACRegressor | 0.11 | 0.15 | 69.40 | 0.33 |
| NuSVR | 0.07 | 0.11 | 70.99 | 0.08 |
| LinearSVR | 0.07 | 0.11 | 71.11 | 0.03 |
| SVR | 0.07 | 0.11 | 71.23 | 0.04 |
| DummyRegressor | 0.05 - | 0.00 | 75.45 | 0.02 |
| DecisionTreeRegressor | 0.13 - | 0.08 | 78.38 | 0.03 |
| ExtraTreeRegressor | 0.18 - | 0.13 | 80.02 | 0.02 |
| GaussianProcessRegressor | 0.99 - | 0.90 | 04.06 | 0.07 |
| MLPRegressor | 1.19 - | 1.09 | 09.17 | 1.34 |
| KernelRidge | 3.91 - | 3.69 | 63.34 | 0.06 |
|-------------------------------------------------------------------------------------|
# Vertical bar plot
rgs.barplot(predictions)
# Horizontal bar plot
rgs.hbarplot(predictions)
FAQs
Introducing Fun Predict, the ultimate time-saver for machine learning! No more complex coding or tedious parameter tuning - just sit back and let Fun Predict build your basic models with ease. It's like having a personal assistant for your machine learning projects, making the process simple, efficient, and, well, Fun! 🛋
We found that funpredict demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
Did you know?
Socket for GitHub automatically highlights issues in each pull request and monitors the health of all your open source dependencies. Discover the contents of your packages and block harmful activity before you install or update your dependencies.
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