How to check models recall score using cross validation in Python?
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# How to check models recall score using cross validation in Python?

This recipe helps you check models recall score using cross validation in Python

0

## Recipe Objective

To test how our model is performing we need a scoring metric and for classifier we can use recall score. Here we will using cross validation to split the data into various set and test the model on a single set while training it on other.

So this is the recipe on how we can check model"s recall score using cross validation in Python.

## Step 1 - Import the library

``` from sklearn.model_selection import cross_val_score from sklearn.tree import DecisionTreeClassifier from sklearn import datasets ```

We have only imported cross_val_score, DecisionTreeClassifier and datasets which is needed.

## Step 2 - Setting up the Data

We have imported an inbuilt breast cancer dataset to train the model. We have stored data in X and target in y. ``` cancer = datasets.load_breast_cancer() X = cancer.data y = cancer.target ```

## Step 3 - Model and cross validation

We have used DecisionTreeClassifier as a model and used cross validation. In cross validation we have passed model, scoring metric as recall and cv as 5.
we have calculated mean and standard deviation of cross validation score. ``` dtree = DecisionTreeClassifier() print(cross_val_score(dtree, X, y, scoring="recall", cv = 5)) mean_score = cross_val_score(dtree, X, y, scoring="recall", cv = 5).mean() std_score = cross_val_score(dtree, X, y, scoring="recall", cv = 5).std() print(mean_score) print(std_score) ``` So the output comes

```[0.90277778 0.93055556 0.95774648 0.95774648 0.84507042]

0.9160015649452269

0.03188194241586345

```

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