How to check models f1 score using cross validation in Python?

This recipe helps you check models f1 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 f1 score to measure our models performance. We will also be using cross validation to test the model on multiple sets of data.

This data science python source code does the following:
1. Classification metrics used for validation of model.
2. Performs train_test_split to seperate training and testing dataset
3. Implements CrossValidation on models and calculating the final result using "F1 Score" method.

So this is the recipe on How we can check model's f1-score using cross validation in Python.

Get Closer To Your Dream of Becoming a Data Scientist with 70+ Solved End-to-End ML Projects

Step 1 - Import the library

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

We have imported various modules from differnt libraries such as cross_val_score, DecisionTreeClassifier and make_classification.

Step 2 - Setting up the Data

We are generating a dataset with make_classification function which will generate a classification dataset as per the passed parameters. X, y = make_classification(n_samples = 10000, n_features = 3, n_informative = 3, n_redundant = 0, n_classes = 2, random_state = 42)

Explore More Data Science and Machine Learning Projects for Practice. Fast-Track Your Career Transition with ProjectPro

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 f1 score for all the test train split. We are printing the f1 score for all the splits in cross validation and we are also printing mean and standard deviation of f1 score. dec_tree = DecisionTreeClassifier() print(cross_val_score(dec_tree, X, y, scoring="f1", cv = 7)) mean_score = cross_val_score(dec_tree, X, y, scoring="f1", cv = 7).mean() std_score = cross_val_score(dec_tree, X, y, scoring="f1", cv = 7).std() print(mean_score) print(std_score) So the output comes as

[0.92254013 0.91392582 0.93802817 0.92426367 0.93614035 0.92210526
 0.9260539 ]

0.9257145721528974

0.006172506932493186

Join Millions of Satisfied Developers and Enterprises to Maximize Your Productivity and ROI with ProjectPro - Read ProjectPro Reviews Now!

Download Materials


What Users are saying..

profile image

Jingwei Li

Graduate Research assistance at Stony Brook University
linkedin profile url

ProjectPro is an awesome platform that helps me learn much hands-on industrial experience with a step-by-step walkthrough of projects. There are two primary paths to learn: Data Science and Big Data.... Read More

Relevant Projects

Ola Bike Rides Request Demand Forecast
Given big data at taxi service (ride-hailing) i.e. OLA, you will learn multi-step time series forecasting and clustering with Mini-Batch K-means Algorithm on geospatial data to predict future ride requests for a particular region at a given time.

BERT Text Classification using DistilBERT and ALBERT Models
This Project Explains how to perform Text Classification using ALBERT and DistilBERT

Hands-On Approach to Master PyTorch Tensors with Examples
In this deep learning project, you will learn how to perform various operations on the building block of PyTorch : Tensors.

Time Series Classification Project for Elevator Failure Prediction
In this Time Series Project, you will predict the failure of elevators using IoT sensor data as a time series classification machine learning problem.

MLOps Project to Deploy Resume Parser Model on Paperspace
In this MLOps project, you will learn how to deploy a Resume Parser Streamlit Application on Paperspace Private Cloud.

Credit Card Default Prediction using Machine learning techniques
In this data science project, you will predict borrowers chance of defaulting on credit loans by building a credit score prediction model.

Time Series Analysis with Facebook Prophet Python and Cesium
Time Series Analysis Project - Use the Facebook Prophet and Cesium Open Source Library for Time Series Forecasting in Python

AWS MLOps Project for ARCH and GARCH Time Series Models
Build and deploy ARCH and GARCH time series forecasting models in Python on AWS .

Customer Market Basket Analysis using Apriori and Fpgrowth algorithms
In this data science project, you will learn how to perform market basket analysis with the application of Apriori and FP growth algorithms based on the concept of association rule learning.

OpenCV Project to Master Advanced Computer Vision Concepts
In this OpenCV project, you will learn to implement advanced computer vision concepts and algorithms in OpenCV library using Python.

OSZAR »