How to use Regression Metrics in Python?

How to use Regression Metrics in Python?

How to use Regression Metrics in Python?

This recipe helps you use Regression Metrics in Python

Recipe Objective

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.

Step 1 - Import the library

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.

Step 2 - Setting up the Data

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 = y = 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)

Step 3 - Training model and calculating Metrics

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
  • 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())
  • Calculating Mean squared 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())
  • Calculating R squared value
  • 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())
So the output comes as:

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

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