如何用几行代码运行 40 个回归模型

# Importing important librariesimport pyforestfrom lazypredict.Supervised import LazyRegressorfrom pandas.plotting import scatter_matrix# Scikit-learn packagesfrom sklearn.linear_model import LinearRegressionfrom sklearn.tree import DecisionTreeRegressorfrom sklearn.ensemble import ExtraTreesRegressorfrom sklearn import metricsfrom sklearn.metrics import mean_squared_error# Hide warningsimport warningswarnings.filterwarnings(“ignore”)# Setting up max columns displayed to 100pd.options.display.max_columns = 100

# Import datasetdf = pd.read_csv('../data/kc_house_data_train.csv', index_col=0)

# Checking datatimes and null valuesdf.info()

# Fixing house with 33 bedroomsdf[df['bedrooms'] == 33] = df[df['bedrooms'] == 3]# This will add 1 bathroom to houses without any bathroomdf['bathrooms'] = df.bedrooms.apply(lambda x: 1 if x < 1 else x)

# Removing nan and infinite valuesdf.replace([np.inf, -np.inf], np.nan, inplace=True)df.dropna(inplace=True)

# Creating train test splitX = df.drop(columns=['price])y = df.price# Call train_test_split on the data and capture the resultsX_train, X_test, y_train, y_test = train_test_split(X, y, random_state=3,test_size=0.25)

reg = LazyRegressor(ignore_warnings=False, custom_metric=None)models, predictions = reg.fit(X_train, X_test, y_train, y_test)print(models)

# Explicitly require this experimental featurefrom sklearn.experimental import enable_hist_gradient_boosting# Now you can import normally from ensemblefrom sklearn.ensemble import HistGradientBoostingRegressor

# Evaluation Functionsdef rmse(model, y_test, y_pred, X_train, y_train):r_squared = model.score(X_test, y_test)mse = mean_squared_error(y_test, y_pred)rmse = np.sqrt(mse)print(‘R-squared: ‘ + str(r_squared))print(‘Mean Squared Error: ‘+ str(rmse))# Create model line scatter plotdef scatter_plot(y_test, y_pred, model_name):plt.figure(figsize=(10,6))sns.residplot(y_test, y_pred, lowess=True, color='#4682b4',line_kws={'lw': 2, 'color': 'r'})plt.title(str('Price vs Residuals for '+ model_name))plt.xlabel('Price',fontsize=16)plt.xticks(fontsize=13)plt.yticks(fontsize=13)plt.show()

# Histogram-based Gradient Boosting Regression Treehist = HistGradientBoostingRegressor()hist.fit(X_train, y_train)y_pred = hist.predict(X_test)