How to check models f1 score using cross validation in Python?
MODEL SELECTION DATA CLEANING PYTHON DATA MUNGING MACHINE LEARNING RECIPES PANDAS CHEATSHEET     ALL TAGS

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

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

This recipe helps you check models f1 score using cross validation in Python

0

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.

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)

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

Relevant Projects

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.

Machine Learning project for Retail Price Optimization
In this machine learning pricing project, we implement a retail price optimization algorithm using regression trees. This is one of the first steps to building a dynamic pricing model.

Predict Churn for a Telecom company using Logistic Regression
Machine Learning Project in R- Predict the customer churn of telecom sector and find out the key drivers that lead to churn. Learn how the logistic regression model using R can be used to identify the customer churn in telecom dataset.

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.

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.

Sequence Classification with LSTM RNN in Python with Keras
In this project, we are going to work on Sequence to Sequence Prediction using IMDB Movie Review Dataset​ using Keras in Python.

Data Science Project in Python on BigMart Sales Prediction
The goal of this data science project is to build a predictive model and find out the sales of each product at a given Big Mart store.

Time Series Forecasting with LSTM Neural Network Python
Deep Learning Project- Learn to apply deep learning paradigm to forecast univariate time series data.

Build an Image Classifier for Plant Species Identification
In this machine learning project, we will use binary leaf images and extracted features, including shape, margin, and texture to accurately identify plant species using different benchmark classification techniques.

Choosing the right Time Series Forecasting Methods
There are different time series forecasting methods to forecast stock price, demand etc. In this machine learning project, you will learn to determine which forecasting method to be used when and how to apply with time series forecasting example.