Based on LazyPredict
from easy_predict import Regressors
from easy_predict import df_to_table
from sklearn.model_selection import train_test_split
from sklearn import datasets
boston = datasets.load_boston()
X, y = boston.data, boston.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
reg = Regressors()
reg.fit(X_train, y_train)
df_to_table(reg.scores(X_test, y_test))Processing... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:00
┏━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━┓
┃ ┃ model ┃ r_squared ┃ adjusted_r_squared ┃ rmse ┃
┡━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━┩
│ 0 │ AdaBoostRegressor │ 0.8649163406391276 │ 0.844960800051726 │ 3.482098831897125 │
│ 1 │ BaggingRegressor │ 0.838297459638218 │ 0.8144095843575002 │ 3.8097642868776838 │
│ 2 │ BayesianRidge │ 0.6597287144165369 │ 0.6094613654098889 │ 5.52652750663049 │
│ 3 │ DecisionTreeRegressor │ 0.6451139869519652 │ 0.5926876441153237 │ 5.643962590723844 │
│ 4 │ DummyRegressor │ -0.0006162634958384317 │ -0.14843457514863267 │ 9.47705636660635 │
│ 5 │ ElasticNet │ 0.5588365719166664 │ 0.4936647018589012 │ 6.292734902446766 │
│ 6 │ ElasticNetCV │ 0.6607461551762512 │ 0.6106291099181975 │ 5.518258921930619 │
│ 7 │ ExtraTreeRegressor │ 0.6276467891067996 │ 0.572640064770304 │ 5.781189917546827 │
│ 8 │ ExtraTreesRegressor │ 0.8481784895131056 │ 0.8257503118275417 │ 3.6915294627714657 │
.
.
Also check
test_easy_predict.py