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# How to use Regression Metrics in Python?

# How to use Regression Metrics in Python?

This recipe helps you use Regression Metrics in Python

In a dataset after applying a regression model how to evaluate it. There are many metrics that we can use. We will be using mean absolute error , mean squared error and R squared.

So this is the recipe on how we we can use Regression Metrics in Python.

```
from sklearn import datasets
from sklearn import tree, model_selection
from sklearn.model_selection import train_test_split
```

We have imported datasets, tree, model_selection and test_train_split which will be needed for the dataset.

We have imported inbuilt wine dataset and stored data in x and target in y. We have used to split the data by test train split. Then we have used model_selection.KFold.
```
seed = 42
dataset = datasets.load_wine()
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)
```

Here we will be using DecisionTreeRegressior as a model
```
model = tree.DecisionTreeRegressor()
```

Now we will be calculating different metrics. We will be using cross validation score to calculate the metrices. So we will be printing the mean and standard deviation of all the scores.

- Calculating Mean Absolute Error
- Calculating Mean squared error
- Calculating R squared value

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

```
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())
```

```
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())
```

Mean Absolute Error: -0.12692307692307692 Standard Deviation: 0.09994715425303413 Mean Squared Error: -0.13351648351648354 Standard Deviation: 0.10845352186546801 R squared val: 0.7997306366386379 Standard Deviation: 0.13923964626776147 â€‹

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