data science

xgboost algorithm


Module - 5 Classification


XGBoost (eXtreme Gradient Boosting) is an open-source software library which provides a gradient boosting framework for C++, Java, Python, R, Julia, Perl, and Scala. It is a machine learning algorithm that yields great results in areas such as classification, regression, and ranking. It is also known as regularized boosting or multiple additive regression trees.


XGBoost is a distributed gradient boosting toolkit that has been tuned for efficient and scalable training of machine learning models. It's an ensemble learning strategy that combines the predictions of several weak models to get a more accurate forecast. Because of its capacity to handle enormous datasets and deliver state-of-the-art performance in various machine learning tasks such as classification and regression, XGBoost has become one of the most popular and commonly used machine learning algorithms. XGBoost's efficient handling of missing values is one of its core advantages, allowing it to handle real-world data with missing values without considerable pre-processing. Moreover, XGBoost has parallel processing capabilities, allowing it to train big datasets.

XGBoost has a wide range of applications, including Kaggle contests, recommendation systems, and click-through rate prediction. It is also extremely adjustable, with the ability to fine-tune numerous model parameters to improve performance.

XgBoost is an acronym for Extreme Gradient Boosting, which was proposed by University of Washington academics. It is a C++ package that optimises the training for Gradient Boosting.

What is XGBoost?

Gradient Boosted decision trees are implemented in XGBoost. In numerous Kaggle competitions, XGBoost models prevail.

This technique generates decision trees in a sequential fashion. Weights are very significant in XGBoost. All of the independent variables are given weights, which are subsequently put into the decision tree, which predicts results.

The weight of factors that the tree predicted incorrectly is raised, and these variables are subsequently put into the second decision tree. These various classifiers/predictors are then combined to form a more powerful and precise model. It can solve issues including regression, classification, ranking, and user-defined prediction.

Optimization and Improvement

Optimization in xgboost is a process by which the machine learning algorithm is tuned to improve its performance. This includes adjusting parameters such as learning rate, tree depth, and regularization strength to achieve the best model for a given data set. Xgboost also includes a number of additional features to help further optimize the model, such as parallelization, cache block tree pruning, cache-awareness, and out-of-score computation.

  1. Regularization is used to reduce overfitting and improve the generalization ability of the model. Xgboost provides a number of regularization parameters, such as L1 and L2 regularization, which can be adjusted to find the best balance between model performance and generalization.
  2. Parallelization is a feature of xgboost that allows the user to utilize multiple cores of a machine in order to speed up the training process. This allows for faster training times and can be used to train larger models.
  3. Cache block tree pruning is a feature which allows the user to reduce the size of the tree by pruning out unnecessary nodes. This reduces the memory required to store the model and can also improve the performance of the model.
  4. Cache-awareness is a feature which allows xgboost to utilize the available memory more efficiently. This is particularly useful when training larger models and can improve the speed of the training process.
  5. Out-of-score computation is a feature which allows xgboost to compute the model’s score more efficiently. This can help reduce the time required to evaluate the model performance and can help improve the overall performance of the model.


  1. XGBoost is fast and efficient. It can handle large datasets with ease and has been proven to be faster than other algorithms.
  2. XGBoost is highly accurate. It has the highest accuracy among all other algorithms and can be tuned to achieve better results.
  3. XGBoost is flexible. It allows for parallel and distributed computing and can run on any platform.
  4. XGBoost provides a number of features to customize your model, including regularization, cross-validation, and early stopping.


  1. XGBoost is a complex algorithm and can be difficult to interpret.
  2. XGBoost can be slow to train due to its many hyperparameters.
  3. XGBoost can be prone to overfitting if not properly tuned.
  4. XGBoost can be memory intensive and is not suitable for low-end systems.

Python implementation

Lets use boston dataset for the demo

Use the already available dataset boston which is in sklearn

import the dataset as “from sklearn.datasets import load_boston”

The Boston housing dataset is included in the Scikit-Learn library. It can be accessed by importing the dataset from the sklearn.datasets module. The dataset contains 506 samples and 13 features. It can be used for both regression and classification tasks. It is a great dataset for practicing machine learning techniques, such as gradient boosting.

# import the necessary modules
import xgboost as xgb
from sklearn.datasets import load_boston
from sklearn.model_selection import train_test_split

# load the boston dataset from sklearn
boston = load_boston()

# split the data into training and test sets
X_train, X_test, y_train, y_test = train_test_split(,, test_size=0.2, random_state=123)

# instantiate an XGBoost regressor
xg_reg = xgb.XGBRegressor(objective ='reg:squarederror', colsample_bytree = 0.3, learning_rate = 0.1,
                max_depth = 5, alpha = 10, n_estimators = 10)

# fit the regressor to the training set,y_train)

# predict on the test set
preds = xg_reg.predict(X_test)

# compute the RMSE
rmse = np.sqrt(mean_squared_error(y_test, preds))
print("RMSE: %f" % (rmse))

This code is an example of how to use an XGBoost regressor on a dataset from sklearn. It begins by loading the boston dataset from sklearn, then it splits the data into training and test sets. Next, it instantiates an XGBoost regressor, fitting it to the training set. Finally, it predicts on the test set and calculates the RMSE (Root Mean Squared Error) which is a measure of how close the model's predictions are to the actual values.


XGBoost is a powerful, flexible, and reliable machine learning library for supervised and unsupervised machine learning tasks. It is an efficient implementation of the gradient boosting algorithm and can be used for both regression and classification problems. XGBoost is easy to use and provides several advantages over other machine learning libraries such as fast training speed, parallel computing capabilities, and excellent performance with large datasets. XGBoost is an excellent choice for any machine learning task and can be used to quickly and accurately build models that can be used in production systems.

Key takeaways

  1. XGBoost is an efficient and powerful algorithm for machine learning.
  2. It is a gradient boosting algorithm that can be used for classification and regression problems.
  3. It is an optimized version of decision tree algorithms and uses a weighted sum of decision trees to make predictions.
  4. XGBoost can be used for hyperparameter optimization to find the best model for a given dataset.
  5. XGBoost has been proven to be more accurate than traditional machine learning algorithms.
  6. XGBoost is highly scalable and can be used for large datasets.
  7. XGBoost is able to deal with missing values and can be used for feature selection.
  8. XGBoost is a fast algorithm that is highly parallelizable, making it suitable for running on large clusters.


1.What is XGBoost? 

  1. A supervised learning algorithm 
  2. An unsupervised learning algorithm 
  3. A deep learning algorithm 
  4. A reinforcement learning algorithm

Answer: A. A supervised learning algorithm

2.XGBoost is used for what type of machine learning tasks?

  1. Regression 
  2. Classification 
  3. Clustering 
  4. Optimization

Answer: B. Classification

3.What is the main purpose of XGBoost? 

  1. To reduce bias 
  2. To improve accuracy 
  3. To reduce variance 
  4. To improve speed

Answer: B. To improve accuracy

4.What is the main advantage of XGBoost? 

  1. It is more accurate than other algorithms 
  2. It is faster than other algorithms 
  3. It is more user friendly than other algorithms 
  4.  It is more flexible than other algorithms

Answer: B. It is faster than other algorithms

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