How to evaluate XGBoost model with learning curves example 1?

How to evaluate XGBoost model with learning curves example 1?

How to evaluate XGBoost model with learning curves example 1?

This recipe helps you evaluate XGBoost model with learning curves example 1

In [2]:
## How to evaluate XGBoost model with learning curves
## DataSet: skleran.datasets.load_breast_cancer()
def Snippet_188():
    print(format('Hoe to evaluate XGBoost model with learning curves','*^82'))

    import warnings

    # load libraries
    import numpy as np
    from xgboost import XGBClassifier
    import matplotlib.pyplot as plt'ggplot')

    from sklearn import datasets
    import matplotlib.pyplot as plt
    from sklearn.model_selection import learning_curve

    # load the datasets
    dataset = datasets.load_breast_cancer()
    X =; y =

    # Create CV training and test scores for various training set sizes
    train_sizes, train_scores, test_scores = learning_curve(XGBClassifier(),
                                               X, y, cv=10, scoring='accuracy', n_jobs=-1,
                                               # 50 different sizes of the training set
                                               train_sizes=np.linspace(0.01, 1.0, 50))

    # Create means and standard deviations of training set scores
    train_mean = np.mean(train_scores, axis=1)
    train_std = np.std(train_scores, axis=1)

    # Create means and standard deviations of test set scores
    test_mean = np.mean(test_scores, axis=1)
    test_std = np.std(test_scores, axis=1)

    # Draw lines
    plt.subplots(1, figsize=(7,7))
    plt.plot(train_sizes, train_mean, '--', color="#111111",  label="Training score")
    plt.plot(train_sizes, test_mean, color="#111111", label="Cross-validation score")

    # Draw bands
    plt.fill_between(train_sizes, train_mean - train_std, train_mean + train_std, color="#DDDDDD")
    plt.fill_between(train_sizes, test_mean - test_std, test_mean + test_std, color="#DDDDDD")

    # Create plot
    plt.title("Learning Curve")
    plt.xlabel("Training Set Size"), plt.ylabel("Accuracy Score"), plt.legend(loc="best")

****************Hoe to evaluate XGBoost model with learning curves****************

Relevant Projects

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.

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.

Deep Learning with Keras in R to Predict Customer Churn
In this deep learning project, we will predict customer churn using Artificial Neural Networks and learn how to model an ANN in R with the keras deep learning package.

Predict Macro Economic Trends using Kaggle Financial Dataset
In this machine learning project, you will uncover the predictive value in an uncertain world by using various artificial intelligence, machine learning, advanced regression and feature transformation techniques.

German Credit Dataset Analysis to Classify Loan Applications
In this data science project, you will work with German credit dataset using classification techniques like Decision Tree, Neural Networks etc to classify loan applications using R.

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

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.

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.

Predict Employee Computer Access Needs in Python
Data Science Project in Python- Given his or her job role, predict employee access needs using amazon employee database.

Anomaly Detection Using Deep Learning and Autoencoders
Deep Learning Project- Learn about implementation of a machine learning algorithm using autoencoders for anomaly detection.