How to optimise number of trees in XGBoost?
MACHINE LEARNING RECIPES DATA CLEANING PYTHON DATA MUNGING PANDAS CHEATSHEET     ALL TAGS

How to optimise number of trees in XGBoost?

How to optimise number of trees in XGBoost?

This recipe helps you optimise number of trees in XGBoost

1

Recipe Objective

Many a times while working on a dataset and using a Machine Learning model we don"t know which set of hyperparameters will give us the best result. Passing all sets of hyperparameters manually through the model and checking the result might be a hectic work and may not be possible to do.

To get the best set of hyperparameters we can use Grid Search. Grid Search passes all combinations of hyperparameters one by one into the model and check the result. Finally it gives us the set of hyperparemeters which gives the best result after passing in the model.

So this recipe is a short example of how can optimise number of trees in XGBoost.

Step 1 - Import the library - GridSearchCv

from sklearn import datasets from sklearn.model_selection import train_test_split from xgboost import XGBClassifier from sklearn.model_selection import GridSearchCV from sklearn.model_selection import StratifiedKFold

Here we have imported various modules like decomposition, datasets, XGBClassifier and GridSearchCV from differnt libraries. We will understand the use of these later while using it in the in the code snipet.
For now just have a look on these imports.

Step 2 - Setup the Data

Here we have used datasets to load the inbuilt iris dataset and we have created objects X and y to store the data and the target value respectively. We have used test_train_split to split the dataset. dataset = datasets.load_iris() X = dataset.data y = dataset.target X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30)

Step 3 - Using GridSearchCV

We have used XGBClassifier as a model. We have created a dictionary param_grid with parameters which we wabt to optimise. Finally we have used GridSearchCV to train and fit. Before using GridSearchCV, lets have a look on the important parameters.

  • estimator: In this we have to pass the models or functions on which we want to use GridSearchCV
  • param_grid: Dictionary or list of parameters of models or function in which GridSearchCV have to select the best.
  • Scoring: It is used as a evaluating metric for the model performance to decide the best hyperparameters, if not especified then it uses estimator score.
model = XGBClassifier() n_estimators = range(50, 400, 50) param_grid = dict(n_estimators=n_estimators) kfold = StratifiedKFold(n_splits=10, shuffle=True, random_state=7) grid_search = GridSearchCV(model, param_grid, scoring="neg_log_loss", n_jobs=-1, cv=kfold) grid_result = grid_search.fit(X, y)`

Step 6 - Printing Results

Now we are using print statements to print the results. It will give the values of hyperparameters as a result. print("Best: %f using %s" % (grid_result.best_score_, grid_result.best_params_)) means = grid_result.cv_results_["mean_test_score"] stds = grid_result.cv_results_["std_test_score"] params = grid_result.cv_results_["params"] print() for mean, stdev, param in zip(means, stds, params): print("%f (%f) with: %r" % (mean, stdev, param)) As an output we get:

Best: -0.157184 using {"n_estimators": 50}

-0.157184 (0.167603) with: {"n_estimators": 50}
-0.183705 (0.201743) with: {"n_estimators": 100}
-0.198009 (0.213457) with: {"n_estimators": 150}
-0.208350 (0.222713) with: {"n_estimators": 200}
-0.215404 (0.230976) with: {"n_estimators": 250}
-0.221676 (0.237353) with: {"n_estimators": 300}
-0.224061 (0.239101) with: {"n_estimators": 350}

Relevant Projects

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.

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.

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.

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

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.

Identifying Product Bundles from Sales Data Using R Language
In this data science project in R, we are going to talk about subjective segmentation which is a clustering technique to find out product bundles in sales data.

Human Activity Recognition Using Multiclass Classification in Python
In this human activity recognition project, we use multiclass classification machine learning techniques to analyse fitness dataset from a smartphone tracker.

Resume parsing with Machine learning - NLP with Python OCR and Spacy
In this machine learning resume parser example we use the popular Spacy NLP python library for OCR and text classification.

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.

Customer Churn Prediction Analysis using Ensemble Techniques
In this machine learning churn project, we implement a churn prediction model in python using ensemble techniques.