AI prompts
base on Lazy Predict help build a lot of basic models without much code and helps understand which models works better without any parameter tuning # Lazy Predict
[](https://pypi.python.org/pypi/lazypredict)
[](https://github.com/shankarpandala/lazypredict/actions/workflows/publish.yml)
[](https://lazypredict.readthedocs.io/en/latest/?badge=latest)
[](https://pepy.tech/project/lazypredict)
[](https://www.codefactor.io/repository/github/shankarpandala/lazypredict)
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>
## Installation
To install Lazy Predict:
```bash
pip install lazypredict
```
## Usage
To use Lazy Predict in a project:
```python
import lazypredict
```
## Classification
Example:
```python
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)
```
| Model | Accuracy | Balanced Accuracy | ROC AUC | F1 Score | Time Taken |
|:-------------------------------|-----------:|--------------------:|----------:|-----------:|-------------:|
| LinearSVC | 0.989474 | 0.987544 | 0.987544 | 0.989462 | 0.0150008 |
| SGDClassifier | 0.989474 | 0.987544 | 0.987544 | 0.989462 | 0.0109992 |
| MLPClassifier | 0.985965 | 0.986904 | 0.986904 | 0.985994 | 0.426 |
| Perceptron | 0.985965 | 0.984797 | 0.984797 | 0.985965 | 0.0120046 |
| LogisticRegression | 0.985965 | 0.98269 | 0.98269 | 0.985934 | 0.0200036 |
| LogisticRegressionCV | 0.985965 | 0.98269 | 0.98269 | 0.985934 | 0.262997 |
| SVC | 0.982456 | 0.979942 | 0.979942 | 0.982437 | 0.0140011 |
| CalibratedClassifierCV | 0.982456 | 0.975728 | 0.975728 | 0.982357 | 0.0350015 |
| PassiveAggressiveClassifier | 0.975439 | 0.974448 | 0.974448 | 0.975464 | 0.0130005 |
| LabelPropagation | 0.975439 | 0.974448 | 0.974448 | 0.975464 | 0.0429988 |
| LabelSpreading | 0.975439 | 0.974448 | 0.974448 | 0.975464 | 0.0310006 |
| RandomForestClassifier | 0.97193 | 0.969594 | 0.969594 | 0.97193 | 0.033 |
| GradientBoostingClassifier | 0.97193 | 0.967486 | 0.967486 | 0.971869 | 0.166998 |
| QuadraticDiscriminantAnalysis | 0.964912 | 0.966206 | 0.966206 | 0.965052 | 0.0119994 |
| HistGradientBoostingClassifier | 0.968421 | 0.964739 | 0.964739 | 0.968387 | 0.682003 |
| RidgeClassifierCV | 0.97193 | 0.963272 | 0.963272 | 0.971736 | 0.0130029 |
| RidgeClassifier | 0.968421 | 0.960525 | 0.960525 | 0.968242 | 0.0119977 |
| AdaBoostClassifier | 0.961404 | 0.959245 | 0.959245 | 0.961444 | 0.204998 |
| ExtraTreesClassifier | 0.961404 | 0.957138 | 0.957138 | 0.961362 | 0.0270066 |
| KNeighborsClassifier | 0.961404 | 0.95503 | 0.95503 | 0.961276 | 0.0560005 |
| BaggingClassifier | 0.947368 | 0.954577 | 0.954577 | 0.947882 | 0.0559971 |
| BernoulliNB | 0.950877 | 0.951003 | 0.951003 | 0.951072 | 0.0169988 |
| LinearDiscriminantAnalysis | 0.961404 | 0.950816 | 0.950816 | 0.961089 | 0.0199995 |
| GaussianNB | 0.954386 | 0.949536 | 0.949536 | 0.954337 | 0.0139935 |
| NuSVC | 0.954386 | 0.943215 | 0.943215 | 0.954014 | 0.019989 |
| DecisionTreeClassifier | 0.936842 | 0.933693 | 0.933693 | 0.936971 | 0.0170023 |
| NearestCentroid | 0.947368 | 0.933506 | 0.933506 | 0.946801 | 0.0160074 |
| ExtraTreeClassifier | 0.922807 | 0.912168 | 0.912168 | 0.922462 | 0.0109999 |
| CheckingClassifier | 0.361404 | 0.5 | 0.5 | 0.191879 | 0.0170043 |
| DummyClassifier | 0.512281 | 0.489598 | 0.489598 | 0.518924 | 0.0119965 |
## Regression
Example:
```python
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)
```
| Model | Adjusted R-Squared | R-Squared | RMSE | Time Taken |
|:------------------------------|---------------------:|------------:|---------:|-------------:|
| ExtraTreesRegressor | 0.378921 | 0.520076 | 54.2202 | 0.121466 |
| OrthogonalMatchingPursuitCV | 0.374947 | 0.517004 | 54.3934 | 0.0111742 |
| Lasso | 0.373483 | 0.515873 | 54.457 | 0.00620174 |
| LassoLars | 0.373474 | 0.515866 | 54.4575 | 0.0087235 |
| LarsCV | 0.3715 | 0.514341 | 54.5432 | 0.0160234 |
| LassoCV | 0.370413 | 0.513501 | 54.5903 | 0.0624897 |
| PassiveAggressiveRegressor | 0.366958 | 0.510831 | 54.7399 | 0.00689793 |
| LassoLarsIC | 0.364984 | 0.509306 | 54.8252 | 0.0108321 |
| SGDRegressor | 0.364307 | 0.508783 | 54.8544 | 0.0055306 |
| RidgeCV | 0.363002 | 0.507774 | 54.9107 | 0.00728202 |
| Ridge | 0.363002 | 0.507774 | 54.9107 | 0.00556874 |
| BayesianRidge | 0.362296 | 0.507229 | 54.9411 | 0.0122972 |
| LassoLarsCV | 0.361749 | 0.506806 | 54.9646 | 0.0175984 |
| TransformedTargetRegressor | 0.361749 | 0.506806 | 54.9646 | 0.00604773 |
| LinearRegression | 0.361749 | 0.506806 | 54.9646 | 0.00677514 |
| Lars | 0.358828 | 0.504549 | 55.0903 | 0.00935149 |
| ElasticNetCV | 0.356159 | 0.502486 | 55.2048 | 0.0478678 |
| HuberRegressor | 0.355251 | 0.501785 | 55.2437 | 0.0129263 |
| RandomForestRegressor | 0.349621 | 0.497434 | 55.4844 | 0.2331 |
| AdaBoostRegressor | 0.340416 | 0.490322 | 55.8757 | 0.0512381 |
| LGBMRegressor | 0.339239 | 0.489412 | 55.9255 | 0.0396187 |
| HistGradientBoostingRegressor | 0.335632 | 0.486625 | 56.0779 | 0.0897055 |
| PoissonRegressor | 0.323033 | 0.476889 | 56.6072 | 0.00953603 |
| ElasticNet | 0.301755 | 0.460447 | 57.4899 | 0.00604224 |
| KNeighborsRegressor | 0.299855 | 0.458979 | 57.5681 | 0.00757337 |
| OrthogonalMatchingPursuit | 0.292421 | 0.453235 | 57.8729 | 0.00709486 |
| BaggingRegressor | 0.291213 | 0.452301 | 57.9223 | 0.0302746 |
| GradientBoostingRegressor | 0.247009 | 0.418143 | 59.7011 | 0.136803 |
| TweedieRegressor | 0.244215 | 0.415984 | 59.8118 | 0.00633955 |
| XGBRegressor | 0.224263 | 0.400567 | 60.5961 | 0.339694 |
| GammaRegressor | 0.223895 | 0.400283 | 60.6105 | 0.0235181 |
| RANSACRegressor | 0.203535 | 0.38455 | 61.4004 | 0.0653253 |
| LinearSVR | 0.116707 | 0.317455 | 64.6607 | 0.0077076 |
| ExtraTreeRegressor | 0.00201902 | 0.228833 | 68.7304 | 0.00626636 |
| NuSVR | -0.0667043 | 0.175728 | 71.0575 | 0.0143399 |
| SVR | -0.0964128 | 0.152772 | 72.0402 | 0.0114729 |
| DummyRegressor | -0.297553 | -0.00265478 | 78.3701 | 0.00592971 |
| DecisionTreeRegressor | -0.470263 | -0.136112 | 83.4229 | 0.00749898 |
| GaussianProcessRegressor | -0.769174 | -0.367089 | 91.5109 | 0.0770502 |
| MLPRegressor | -1.86772 | -1.21597 | 116.508 | 0.235267 |
| KernelRidge | -5.03822 | -3.6659 | 169.061 | 0.0243919 |", Assign "at most 3 tags" to the expected json: {"id":"8264","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"