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

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

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

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

This data science python source code does the following: 1.Classification metrics used for validation of model. 2. Imports the necessary libraries nad modules. 3. Implements of CrossValidation on models and calculating the final result using "Recall" method.
In [1]:
## How to check model's recall score using cross validation in Python
def Snippet_135():
    print(format('How to check model\'s recall score using cross validation in Python','*^82'))

    import warnings

    # load libraries
    from sklearn.model_selection import cross_val_score
    from sklearn.tree import DecisionTreeClassifier
    from sklearn.datasets import make_classification

    # Generate features matrix and target vector
    X, y = make_classification(n_samples = 10000,
                               n_features = 3,
                               n_informative = 3,
                               n_redundant = 0,
                               n_classes = 2,
                               random_state = 42)

    # Create Decision Tree model
    dtree = DecisionTreeClassifier()

    # Cross-validate model using accuracy
    print(); print(cross_val_score(dtree, X, y, scoring="recall", cv = 7))
    mean_score = cross_val_score(dtree, X, y, scoring="recall", cv = 7).mean()
    std_score = cross_val_score(dtree, X, y, scoring="recall", cv = 7).std()
    print(); print(mean_score)
    print(); print(std_score)

********How to check model's recall score using cross validation in Python********

[0.92307692 0.91748252 0.92577031 0.91736695 0.93137255 0.91316527



Relevant Projects

Forecast Inventory demand using historical sales data in R
In this machine learning project, you will develop a machine learning model to accurately forecast inventory demand based on historical sales data.

Data Science Project on Wine Quality Prediction in R
In this R data science project, we will explore wine dataset to assess red wine quality. The objective of this data science project is to explore which chemical properties will influence the quality of red wines.

Data Science Project - Instacart Market Basket Analysis
Data Science Project - Build a recommendation engine which will predict the products to be purchased by an Instacart consumer again.

PySpark Tutorial - Learn to use Apache Spark with Python
PySpark Project-Get a handle on using Python with Spark through this hands-on data processing spark python tutorial.

Mercari Price Suggestion Challenge Data Science Project
Data Science Project in Python- Build a machine learning algorithm that automatically suggests the right product prices.

Music Recommendation System Project using Python and R
Machine Learning Project - Work with KKBOX's Music Recommendation System dataset to build the best music recommendation engine.

Predict Credit Default | Give Me Some Credit Kaggle
In this data science project, you will predict borrowers chance of defaulting on credit loans by building a credit score prediction model.

Human Activity Recognition Using Smartphones Data Set
In this deep learning project, you will build a classification system where to precisely identify human fitness activities.

Solving Multiple Classification use cases Using H2O
In this project, we are going to talk about H2O and functionality in terms of building Machine Learning models.

Learn to prepare data for your next machine learning project
Text data requires special preparation before you can start using it for any machine learning project.In this ML project, you will learn about applying Machine Learning models to create classifiers and learn how to make sense of textual data.