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- What is ModuleNotFoundError: No module named ‘xgboost’?
- How to fix ModuleNotFoundError: No module named ‘xgboost’?
In Python, ModuleNotFoundError: No module named ‘xgboost’ error occurs if we try to import the ‘xgboost‘ module without installing the package or if you have not installed it in the correct environment.
In this tutorial, let’s look at installing the
xgboost module correctly in different operating systems and solve ModuleNotFoundError: No module named ‘xgboost’ error.
What is ModuleNotFoundError: No module named ‘xgboost’?
There are various reasons why we get the ModuleNotFoundError: No module named ‘xgboost’ error
- Trying to use the module without installing the xgboost package.
- If the IDE is set to the incorrect version of the Python/Python interpreter.
- You are using the virtual environment and the xgboost module is not installed inside a virtual environment
- Installing the xgboost package in a different version of Python than the one which is used currently.
- Declaring a variable name as the module name(xgboost)
If you are getting an error installing pip, checkout pip: command not found to resolve the issue.
How to fix ModuleNotFoundError: No module named ‘xgboost’?
xgboost is not a built-in module (it doesn’t come with the default python installation) in Python; you need to install it explicitly using the pip installer and then use it.
XGBoost is an optimized distributed gradient boosting library designed to be highly efficient, flexible, and portable. It implements machine learning algorithms under the Gradient Boosting framework. XGBoost provides a parallel tree boosting (also known as GBDT, GBM) that solves many data science problems in a fast and accurate way. The same code runs on a major distributed environment (Hadoop, SGE, MPI) and can solve problems beyond billions of examples.
We can fix the error by installing the ‘xgboost‘ module by running the
pip install xgboost command in your terminal/shell.
We can verify if the package is installed correctly by running the following command in the terminal/shell.
This will provide the details of the package installed, including the version number, license, and the path it is installed. If the module is not installed, you will get a warning message in the terminal stating WARNING: Package(s) not found: xgboost.
pip show xgboost
Name: xgboost Version: 1.6.1 Summary: XGBoost Python Package Home-page: https://github.com/dmlc/xgboost Author: Author-email: License: Apache-2.0 Location: c:\personal\ijs\python_samples\venv\lib\site-packages Requires: scipy, numpy
Solution 1 – Installing and using the xgboost module in a proper way
Based on the Python version and the operating system you are running, run the relevant command to install the xgboost module.
# If you are using Python 2 (Windows) pip install xgboost # if you are using Python 3 (Windows) pip3 install xgboost # If the pip is not set as environment varibale PATH python -m pip install xgboost # If you are using Python 2 (Linux) sudo pip install xgboost # if you are using Python 3 (Linux) sudo pip3 install xgboost # In case if you have to easy_install sudo easy_install -U xgboost # On Centos yum install xgboost # On Ubuntu sudo apt-get install xgboost # If you are installing it in Anaconda conda install -c conda-forge xgboost
Once you have installed the xgboost module, we can now import it inside our code and use it as shown below.
# First XGBoost model for Pima Indians dataset from numpy import loadtxt from xgboost import XGBClassifier from sklearn.model_selection import train_test_split from sklearn.metrics import accuracy_score # load data dataset = loadtxt("pima-indians-diabetes.csv", delimiter=",") # split data into X and y X = dataset[:, 0:8] Y = dataset[:, 8] # split data into train and test sets seed = 7 test_size = 0.33 X_train, X_test, y_train, y_test = train_test_split( X, Y, test_size=test_size, random_state=seed ) # fit model no training data model = XGBClassifier() model.fit(X_train, y_train) # make predictions for test data y_pred = model.predict(X_test) predictions = [round(value) for value in y_pred] # evaluate predictions accuracy = accuracy_score(y_test, predictions) print("Accuracy: %.2f%%" % (accuracy * 100.0))
Solution 2 – Verify if the IDE is set to use the correct Python version
If you are still getting the same error even after installing the package, you can verify if the IDE you are using is configured with the correct version of the Python interpreter.
For Eg:- In the case of Visual Studio Code, we can set the Python version by pressing
CTRL + Shift + Por (
P on Mac) to open the command palette.
Once the command palette opens, select the Python interpreter and select the correct version of Python and also the virtual environment(if configured) as shown below.
Solution 3 – Installing xgboost inside the virtual environment
Many different IDEs like Jupyter Notebook, Spyder, Anaconda, or PyCharm often install their own virtual environment of Python to keep things clean and separated from your global Python.
If you are using VS Code, then you can also create a virtual environment, as shown below.
In the case of virtual environments, you need to ensure that the xgboost module needs to be installed inside the virtual environment and not globally.
Step 1: Create a Virtual Environment. If you have already created a virtual environment, then proceed to step 2.
Step 2: Activate the Virtual Environment
Step 3: Install the required module using the
pip install command
# Create a virtual Environment py -3 -m venv venv # Activate the virtual environment (windows command) venv\Scripts\activate.bat # Activate the virtual environment (windows powershell) venv\Scripts\Activate.ps1 # Activate the virtual environment (Linux) source venv/bin/activate # Install xgboost inside the virtual environment pip install xgboost
Solution 4 – Ensure that a module name is not declared name a variable name.
Last but not least, you may need to cross-check and ensure that you haven’t declared a variable with the same name as the module name.
You should check if you haven’t named any files as
xgboost.py as it may shadow the original xgboost module.
If the issue is still not solved, you can try removing the package and installing it once again, restart the IDE, and check the paths to ensure that packages are installed in the correct environment path and Python version.
The ModuleNotFoundError: No module named ‘xgboost’ error occurs when we try to import the ‘xgboost‘ module without installing the package or if you have not installed it in the correct environment.
We can resolve the issue by installing the xgboost module by running the
pip install xgboost command. Also, ensure that the module is installed in the proper environment in case you use any virtual environments, and the Python version is appropriately set in the IDE that you are running the code.