How to check models precision score using cross validation in Python?
This recipe helps you check models precision score using cross validation in Python
Recipe Objective
After training a model we need a measure to check its performance, their are many scoring metric on which we can score the model"s performance. Out of many metric we will be using precision to measure our models performance. We will also be using cross validation to test the model on multiple sets of data.
So this is the recipe on How we can check model"s precision score using cross validation in Python.
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Table of Contents
Step 1 - Import the library
from sklearn.model_selection import cross_val_score
from sklearn.tree import DecisionTreeClassifier
from sklearn.datasets import make_classification
from sklearn import datasets
We have imported various modules from different libraries such as cross_val_score, DecisionTreeClassifier, datasets and make_classification.
Step 2 - Setting up the Data
We have used an inbuilt cancer dataset. 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 its accuracy
We are using DecisionTreeClassifier as a model to train the data. We are training the model with cross_validation which will train the data on different training set and it will calculate precision for all the test train split. We are printing the precision for all the splits in cross validation. We have passed model, data, target and cv an parameters. cv signifies the number of splits we want while performing cross validation. We are also printing mean and standard deviation of precision.
dtree = DecisionTreeClassifier()
print(cross_val_score(dtree, X, y, scoring="precision", cv = 7))
mean_score = cross_val_score(dtree, X, y, scoring="precision", cv = 7).mean()
std_score = cross_val_score(dtree, X, y, scoring="precision", cv = 7).std()
print(mean_score)
print(std_score)
So the output comes as
[0.94117647 0.90566038 0.94 0.94339623 0.96078431 0.97959184
0.95833333]
0.9489445085248625
0.022393630261369132
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