How to use Regression Metrics in Python?
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# How to use Regression Metrics in Python?

This recipe helps you use Regression Metrics in Python
In :
```## How to use Regression Metrics in Python
def Snippet_182():
print()
print(format('How to use Regression Metrics in Python','*^82'))
import warnings
warnings.filterwarnings("ignore")

from sklearn import datasets
from sklearn import tree, model_selection
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt

plt.style.use('ggplot')

seed = 42
X = dataset.data; y = dataset.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25)
kfold = model_selection.KFold(n_splits=10, random_state=seed)
# fit a tree.DecisionTreeClassifier() model to the data
model = tree.DecisionTreeRegressor()

# metrics -> Mean Absolute Error
scoring = 'neg_mean_absolute_error'
results = model_selection.cross_val_score(model, X_train, y_train, cv=kfold, scoring=scoring)
print(); print("Mean Absolute Error: ", results.mean()); print("Standard Deviation: ", results.std())

# metrics -> Mean Squred Error
scoring = 'neg_mean_squared_error'
results = model_selection.cross_val_score(model, X_train, y_train, cv=kfold, scoring=scoring)
print(); print("Mean Squared Error: ", results.mean()); print("Standard Deviation: ", results.std())

# metrics -> R squared
scoring = 'r2'
results = model_selection.cross_val_score(model, X_train, y_train, cv=kfold, scoring=scoring)
print(); print("R squared val: ", results.mean()); print("Standard Deviation: ", results.std())
Snippet_182()
```
```*********************How to use Regression Metrics in Python**********************

Mean Absolute Error:  -3.0952560455192035
Standard Deviation:  0.4539146116435922

Mean Squared Error:  -21.0677652916074
Standard Deviation:  9.255250927975112

R squared val:  0.7561802285707946
Standard Deviation:  0.1692887494698974
```