python lazypredict构建大量基本模型简化机器学习

pip install lazypredict

import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from lazypredict.Supervised import LazyRegressor
housing_data = pd.read_csv('Housing.csv')
housing_data

# dropping the target column from the input feature
x_data = housing_data.drop('price', axis=1)

# assigning the target feature 
y_data = housing_data['price']

# splitting the data to train and test set
X_train, X_test, y_train, y_test = train_test_split(x_data, y_data,test_size=.2,random_state =123)

lzy_regressor = LazyRegressor(verbose=0,ignore_warnings=True, custom_metric=None, predictions=True, regressors ='all' )
regressor_model,predictions = lzy_regressor.fit(X_train, X_test, y_train, y_test)
regressor_model

# load the data
stroke_data = pd.read_csv('healthcare-dataset-stroke-data.csv')
stroke_data= stroke_data.drop('id', axis =1) # remove unnecessary column
stroke_data

from lazypredict.Supervised import LazyClassifier

# defining x_input and y_target 
x_data = stroke_data.drop('stroke', axis=1)
y_data = stroke_data['stroke']

# train-test split
X_train, X_test, y_train, y_test = train_test_split(x_data, y_data,test_size=0.2,random_state =123)

# define the lazyclassifiy model and run
lzy_classifier = LazyClassifier(verbose=0,ignore_warnings=True, custom_metric=None, predictions=True, classifiers='all' )
classifier_model ,predictions = lzy_classifier.fit(X_train, X_test, y_train, y_test)
classifier_model