Lazy Predict

https://lazypredict.readthedocs.io/en/latest/
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Lazy Predict is a Python library that automatically builds and compares multiple machine learning...
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### Install from source

Source: https://lazypredict.readthedocs.io/en/latest/_sources/installation.rst.txt

Execute this command after downloading or cloning the source code to install the package.

```console
$ python setup.py install
```

--------------------------------

### Install Lazy Predict using pip

Source: https://lazypredict.readthedocs.io/en/latest/installation.html

Use this command to install the latest stable release of Lazy Predict. Ensure pip is installed.

```bash
pip install lazypredict

```

--------------------------------

### Install Lazy Predict from source

Source: https://lazypredict.readthedocs.io/en/latest/installation.html

After cloning the repository or downloading the tarball, use this command to install Lazy Predict from the local source files.

```bash
python setup.py install

```

--------------------------------

### Install Lazy Predict via pip

Source: https://lazypredict.readthedocs.io/en/latest/_sources/installation.rst.txt

Use this command to install the most recent stable release of Lazy Predict.

```console
$ pip install lazypredict
```

--------------------------------

### Clone Lazy Predict repository

Source: https://lazypredict.readthedocs.io/en/latest/installation.html

Clone the Lazy Predict GitHub repository to install from source. This command fetches the public repository.

```bash
git clone git://github.com/shankarpandala/lazypredict

```

--------------------------------

### Download Lazy Predict tarball

Source: https://lazypredict.readthedocs.io/en/latest/installation.html

Download the tarball of the Lazy Predict repository from GitHub to install from source. This command fetches the master branch.

```bash
curl -OJL https://github.com/shankarpandala/lazypredict/tarball/master

```

--------------------------------

### Download Lazy Predict tarball

Source: https://lazypredict.readthedocs.io/en/latest/_sources/installation.rst.txt

Download the master branch tarball directly from the repository.

```console
$ curl -OJL https://github.com/shankarpandala/lazypredict/tarball/master
```

--------------------------------

### Clone Lazy Predict repository

Source: https://lazypredict.readthedocs.io/en/latest/_sources/installation.rst.txt

Clone the public repository from GitHub to obtain the source code.

```console
$ git clone git://github.com/shankarpandala/lazypredict
```

--------------------------------

### Perform basic classification with LazyClassifier

Source: https://lazypredict.readthedocs.io/en/latest/_sources/examples.rst.txt

Initializes and fits a LazyClassifier on the breast cancer dataset to compare multiple models.

```python
from lazypredict.Supervised import LazyClassifier
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split

# Load data
data = load_breast_cancer()
X = data.data
y = data.target

# Split data
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=123)

# Create classifier
clf = LazyClassifier(verbose=0, ignore_warnings=True, custom_metric=None)

# Fit and get models
models, predictions = clf.fit(X_train, X_test, y_train, y_test)
print(models)
```

--------------------------------

### Perform basic regression with LazyRegressor

Source: https://lazypredict.readthedocs.io/en/latest/_sources/examples.rst.txt

Initializes and fits a LazyRegressor on the diabetes dataset to evaluate various regression models.

```python
from lazypredict.Supervised import LazyRegressor
from sklearn.datasets import load_diabetes
from sklearn.model_selection import train_test_split

# Load data
diabetes = load_diabetes()
X = diabetes.data
y = diabetes.target

# Split data
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=123)

# Create and fit regressor
reg = LazyRegressor(verbose=0, ignore_warnings=True, custom_metric=None)
models, predictions = reg.fit(X_train, X_test, y_train, y_test)
print(models)
```

--------------------------------

### Run Regression Models

Source: https://lazypredict.readthedocs.io/en/latest/_sources/usage.rst.txt

Trains and evaluates multiple regression models using the LazyRegressor class.

```python
from lazypredict.Supervised import LazyRegressor
from sklearn import datasets
from sklearn.utils import shuffle
import numpy as np
boston = datasets.load_boston()
X, y = shuffle(boston.data, boston.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)
```

--------------------------------

### Import Lazy Predict

Source: https://lazypredict.readthedocs.io/en/latest/_sources/usage.rst.txt

Basic import statement required to access the library functionality.

```python
import lazypredict
```

--------------------------------

### Integrate Lazy Predict with MLflow

Source: https://lazypredict.readthedocs.io/en/latest/_sources/examples.rst.txt

Enables automatic experiment tracking by setting the MLFLOW_TRACKING_URI environment variable.

```python
import os
os.environ['MLFLOW_TRACKING_URI'] = 'sqlite:///mlflow.db'

# MLflow tracking will be automatically enabled
reg = LazyRegressor(verbose=0, ignore_warnings=True)
models, predictions = reg.fit(X_train, X_test, y_train, y_test)
# All metrics will be logged to MLflow automatically
```

--------------------------------

### Run Classification Models

Source: https://lazypredict.readthedocs.io/en/latest/_sources/usage.rst.txt

Trains and evaluates multiple classification models using the LazyClassifier class.

```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=.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)
models
```

--------------------------------

### Use Pandas DataFrames with Lazy Predict

Source: https://lazypredict.readthedocs.io/en/latest/_sources/examples.rst.txt

Demonstrates compatibility with pandas DataFrames and Series for feature and target inputs.

```python
import pandas as pd

# Your DataFrame
df = pd.DataFrame(X, columns=diabetes.feature_names)

# Split features and target
X = df
y = pd.Series(diabetes.target)

# Rest remains the same
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
reg = LazyRegressor(verbose=0, ignore_warnings=True)
models, predictions = reg.fit(X_train, X_test, y_train, y_test)
```

--------------------------------

### Access trained model objects

Source: https://lazypredict.readthedocs.io/en/latest/_sources/examples.rst.txt

Retrieves the underlying trained model objects from the regressor for individual use or prediction.

```python
# Get all trained models
model_dictionary = reg.provide_models(X_train, X_test, y_train, y_test)

# Access specific model
random_forest = model_dictionary['RandomForestRegressor']

# Make predictions with specific model
predictions = random_forest.predict(X_test)
```

--------------------------------

### Apply custom metrics to LazyClassifier

Source: https://lazypredict.readthedocs.io/en/latest/_sources/examples.rst.txt

Passes a custom evaluation function to the custom_metric parameter during classifier initialization.

```python
from sklearn.metrics import f1_score

def custom_f1(y_true, y_pred):
    return f1_score(y_true, y_pred, average='weighted')

clf = LazyClassifier(verbose=0, ignore_warnings=True, custom_metric=custom_f1)
models, predictions = clf.fit(X_train, X_test, y_train, y_test)
```
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