Basics of machine learning

Authors: AvatarAnup Kumar
  • What is machine learning?

  • Why is it useful?

  • What are its different approaches?

  • Provide the basics of machine learning and its variants.

  • Learn how to do classification using the training and test data.

  • Learn how to use Galaxy’s machine learning tools.

Time estimation: 30 minutes
Supporting Materials:
Last modification: Oct 18, 2022
License: Tutorial Content is licensed under Creative Commons Attribution 4.0 International License The GTN Framework is licensed under MIT


Machine learning uses techniques from statistics, mathematics and computer science to make computer programs learn from data. It is one of the most popular fields of computer science and finds applications in multiple streams of data analysis such as classification, regression, clustering, dimensionality reduction, density estimation and many more. Some real-life applications are spam filtering, medical diagnosis, autonomous driving, recommendation systems, facial recognition, stock prices prediction and many more. The following image shows a basic flow of any machine learning task. Data is provided by a user to a machine learning algorithm for analysis.

Figure 1: Flow of a machine learning task.

There are multiple ways in which machine learning can be used to perform data analysis. They depend on the nature of data and the kind of data analysis. The following image shows the most popular ones. In supervised learning techniques, the categories of data records are known beforehand. But in unsupervised learning, the categories of data records are not known.

Figure 2: Different types of machine learning.

In general, machine learning can be used in multiple real-life tasks by applying its variants as depicted in the following image.

Figure 3: Real-life usage of machine learning.

The following image shows how a classification task is performed. The complete data is divided into training and test sets. The training set is used by a classifier to learn features. It results in a trained model and its robustness (of learning) is evaluated using the test set (unseen by the classifier during the training).

Figure 4: Supervised learning.

This tutorial shows how to use a machine learning module implemented as a Galaxy tool. The data used in this tutorial is available at Zenodo.


Performing a machine learning task (classification) using a tool involves the following steps:

  1. Introduction
  2. Data upload
  3. Train a classifier
  4. Predict using a trained model
  5. See predictions

Data upload

The datasets required for this tutorial contain 9 features of breast cells which include the thickness of clump, cell-size, cell-shape and so on (more information). In addition to these features, the training dataset contains one more column as target. It has a binary value (0 or 1) for each row. 0 indicates no breast cancer and 1 indicates breast cancer. The test dataset does not contain the target column.

Hands-on: Data upload
  1. Create a new history for this tutorial

    Click the new-history icon at the top of the history panel.

    If the new-history is missing:

    1. Click on the galaxy-gear icon (History options) on the top of the history panel
    2. Select the option Create New from the menu
  2. Import the following datasets and choose the type of data as tabular.
    • Copy the link location
    • Open the Galaxy Upload Manager (galaxy-upload on the top-right of the tool panel)

    • Select Paste/Fetch Data
    • Paste the link into the text field

    • Press Start

    • Close the window
  3. Rename datasets to breast-w_train and breast-w_test.

    • Click on the galaxy-pencil pencil icon for the dataset to edit its attributes
    • In the central panel, change the Name field
    • Click the Save button
  4. The datasets should look like these:

    Figure 5: Training data (breast-w_train) with targets (9 features and one target).
    Figure 6: Test data (breast-w_test) (9 features and no target).

Train a classifier

In this step, we will use the SVM (support vector machine) classifier for training on the breast-w_train dataset. The classifier learns a mapping between each row and its category. SVM is a memory efficient classifier which needs only those data points which lie on the decision boundaries among different classes to predict a class for a new sample. The rest of the data points can thrown away. We will use the LinearSVC variant of SVM which is faster. Other variants SVC and NuSVC have high running time for large datasets. The last column of the training dataset contains a category/class for each row. The classifier learns a mapping between data row and its category which is called a trained model. The trained model is used to predict the categories of the unseen data.

Hands-on: Train a classifier

Support vector machines (SVMs) for classification tool with the following parameters to train:

  • “Select a Classification Task”: Train a model
    • “Classifier type”: Linear Support Vector Classification
    • “Select input type”: tabular data
    • param-file “Training samples dataset”: breast-w_train tabular file
    • “Does the dataset contain header”: Yes
    • “Choose how to select data by column”: All columns EXCLUDING some by column header name(s)
    • “Type header name(s)”: target
    • param-file “Dataset containing class labels or target values”: breast-w_train tabular file
    • “Does the dataset contain header”: Yes
    • “Choose how to select data by column”: Select columns by column header name(s)
    • “Type header name(s):”: target

Predict using a trained model

The previous step produced a trained model (zip file) which we will use to predict classes for the test data (breast-w_test).

Hands-on: Predict using a trained model

Support vector machines (SVMs) for classification tool with the following parameters

  • “Select a Classification Task”: Load a model and predict
    • param-file “Models”: Zipped file (output of Support vector machines (SVMs) for classification tool)
    • param-file “Data (tabular)”: breast-w_test file
    • “Does the dataset contain header”: Yes
    • “Select the type of prediction”: Predict class labels

See predictions

The last column of the predicted dataset shows the category of each row. A row either gets 0 (no breast cancer) or 1 (breast cancer) as its predicted category.

Hands-on: See the predicted column
  1. Click on view data link of the dataset created after executing the previous step.
  2. The last column of the tabular data shows the predicted category (target) for each row.

Read more about machine learning using scikit-learn here.

Key points
  • Machine learning algorithms learn features from data.

  • It is used for multiple tasks such as classification, regression, clustering and so on.

  • Multiple learning tasks can be performed using Galaxy’s machine learning tools.

  • For the classification and regression tasks, data is divided into training and test sets.

  • Each sample/record in the training data has a category/class/label.

  • A machine learning algorithm learns features from the training data and do predictions on the test data.

Frequently Asked Questions

Have questions about this tutorial? Check out the tutorial FAQ page or the FAQ page for the Statistics and machine learning topic to see if your question is listed there. If not, please ask your question on the GTN Gitter Channel or the Galaxy Help Forum


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Citing this Tutorial

  1. Anup Kumar, Basics of machine learning (Galaxy Training Materials). Online; accessed TODAY
  2. Batut et al., 2018 Community-Driven Data Analysis Training for Biology Cell Systems 10.1016/j.cels.2018.05.012

author = "Anup Kumar",
title = "Basics of machine learning (Galaxy Training Materials)",
year = "",
month = "",
day = ""
url = "\url{}",
note = "[Online; accessed TODAY]"
    doi = {10.1016/j.cels.2018.05.012},
    url = {},
    year = 2018,
    month = {jun},
    publisher = {Elsevier {BV}},
    volume = {6},
    number = {6},
    pages = {752--758.e1},
    author = {B{\'{e}}r{\'{e}}nice Batut and Saskia Hiltemann and Andrea Bagnacani and Dannon Baker and Vivek Bhardwaj and Clemens Blank and Anthony Bretaudeau and Loraine Brillet-Gu{\'{e}}guen and Martin {\v{C}}ech and John Chilton and Dave Clements and Olivia Doppelt-Azeroual and Anika Erxleben and Mallory Ann Freeberg and Simon Gladman and Youri Hoogstrate and Hans-Rudolf Hotz and Torsten Houwaart and Pratik Jagtap and Delphine Larivi{\`{e}}re and Gildas Le Corguill{\'{e}} and Thomas Manke and Fabien Mareuil and Fidel Ram{\'{\i}}rez and Devon Ryan and Florian Christoph Sigloch and Nicola Soranzo and Joachim Wolff and Pavankumar Videm and Markus Wolfien and Aisanjiang Wubuli and Dilmurat Yusuf and James Taylor and Rolf Backofen and Anton Nekrutenko and Björn Grüning},
    title = {Community-Driven Data Analysis Training for Biology},
    journal = {Cell Systems}

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