# Machine learning: classification and regression

### Overview

question Questions
• what are classification and regression techniques?
• How they can be used for prediction?
• How visualizations can be used to analyze predictions?
objectives Objectives
• Explain the types of supervised machine learning - classification and regression.
• Learn how to make predictions using the training and test data.
• Visualize the predictions.
requirements Requirements

time Time estimation: 1 hour

Supporting Materials

# Introduction

Supervised learning methods in machine learning have targets/classes/categories defined in the datasets. These targets can either be discrete values (integers) or real-values (continuous). When the targets are discrete, the learning task is known as classification. These discrete values are called classes or categories. When the targets are real-values, the task becomes regression. Classification is assigning a category to each sample in the dataset. Regression assigns a real-valued output or target to each sample. In figure 1, the line is a boundary which separates blue balls from violet ones. The task of a classification method is to learn this boundary which can be used to differentiate between unseen balls. The line is the decision boundary which determines the category of a new ball.

Figure 2 shows how a (regression) curve is fit which explains most of the data points (blue balls). Here, the curve is a straight line (red). The regression task is to learn this curve which explains the underlying distribution of the data points.

### Agenda

In this tutorial, we will deal with:

1. Classification
2. Regression
3. Conclusion

# Classification

Classification task assigns a category/class to a sample by learning a decision boundary using a dataset. This dataset is called a training dataset and contains a class/category for each sample. The algorithm which performs this task is called a classifier. The training dataset contains “features” as columns and a mapping between these features and the target is learned for each sample. The performance of mapping is evaluated using test dataset. The test dataset contains only the feature columns and not the target column. The target column is predicted using the mapping learned on the training dataset. In this tutorial, we will use a classifier to train a model using a training dataset, predict the targets for test dataset and visualize the results using plots.

The datasets required for this tutorial contain 9 features of breast cancer 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. The third dataset contains all the samples from test dataset but also the target column which would be needed to create a plot showing the comparison between actual and predicted targets.

1. Create a new history for this tutorial

### tip Tip: Creating a new history

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

https://zenodo.org/api/files/efd372b1-4d11-4f43-bba6-66e75a0b4d15/breast-w_targets.tsv
https://zenodo.org/api/files/efd372b1-4d11-4f43-bba6-66e75a0b4d15/breast-w_test.tsv
https://zenodo.org/api/files/efd372b1-4d11-4f43-bba6-66e75a0b4d15/breast-w_train.tsv

• 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

By default, Galaxy uses the URL as the name, so rename the files with a more useful name.

3. Rename datasets to breast-w_train, breast-w_test and breast-w_targets

### tip Tip: Renaming a dataset

• 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

## Learn using training data

Using the dataset breast-w_train, SVM (support vector machine) classifier is trained which learns features from the data and maps them to the targets. This mapping is called a trained model. The training step produces a model file of type zip.

### hands_on Hands-on: Train the model

1. Support vector machines (SVMs) for classification tool with the following parameters to train the classifier on training data:
• “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
• “Does the dataset contain header”: Yes
• “Choose how to select data by column”: All columns but by column header name(s)
• “Type header name(s)”: target
• param-file “Dataset containing class labels”: breast-w_train
• “Does the dataset contain header”: Yes
• “Choose how to select data by column”: Select columns by column header name(s)
• “Select target column(s)”: target
2. Rename the generated file to model

## Predict using test data

The model learned in the previous step can now be used to predict the categories of unseen test (breast-w_test) data. This step assigns a category to each row in breast-w_test dataset.

### hands_on Hands-on: Predict categories using the model

1. Support vector machines (SVMs) for classification tool with the following parameters to predict classes of test data using the trained model:
• “Select a Classification Task”: Load a model and predict
• param-file “Models”: model (output of the previous step)
• param-file “Data (tabular)”: breast-w_test
• param-check “Does the dataset contain header”: Yes
• param-select “Select the type of prediction”: Predict class labels
2. Rename the generated file to predicted_labels

## Visualise the prediction

After the training and prediction tasks, we should check whether the predictions are correct. We will use another dataset breast-w_targets for this verification. It is similar to the test dataset (breast-w_test) but contains an extra target column having the true targets of the test data. With the predicted and true targets, the learned model is evaluated to verify how good the predictions are. To visualise these predictions, a plotting tool is used.

### hands_on Hands-on: Check and visualize the predictions

1. Plot confusion matrix, precision, recall and ROC and AUC curves tool with the following parameters to visualise the predictions:
• param-file “Select input data file”: breast-w_targets
• param-file “Select predicted data file”: predicted_labels
• param-file “Select trained model”: model

The tool creates the following three plots:

1. Confusion matrix of the correctly and incorrectly predicted samples:

In figure 3, the diagonal from bottom-left to top-right shows the number of correctly predicted labels and the diagonal from top-left to bottom-right shows the number of incorrectly predicted samples.

2. Precision, recall and F1 score:

These scores determine the robustness of classification. In figure 4, the recall curve shows the percentage of correctly predicted samples per class. All these curves converge because all the samples in breast-w_test file get correctly classified.

3. Receiver operator characteristics (ROC) and area under ROC (AUC):

The blue curve in figure 5 shows the ROC curve. When it is close to the orange curve (y = x), the classification results are not good. When it is more towards the top-left (like the blue curve shown in the plot), the classification performance is good.

By following these steps, we learn how to perform classification and visualise the predictions using Galaxy machine learning and plotting tools. The classes of unseen (test) data are predicted, evaluated against the true classes and visualized to show how good is the classification.

# Regression

Regression is also a supervised learning task where target is a real number (continuous) instead of discrete like in classification. The algorithms which are used for regression tasks are called regressors. A regressor learns the mapping between the features of a dataset row and its target value. Inherently, it tries to fit a curve for the targets. This curve can be linear (straight line curve) or non-linear.

The datasets required for this tutorial contain 21 features of computer system activity which include columns like fork, exec and so on (more information). In addition to these features, the training dataset contains one more column as target which contains a real number for each row. All the values in the datasets are real numbers. The dataset train_data.tabular is used for training a regressor which maps the features to the targets. The test (unseen) dataset test_data.tabular is used to predict a target value for each row. The dataset test_target.tabular is used to evaluate the quality of predictions as it is also the test data along with the true targets. A plotting tool is used to demonstrate the difference between true and predicted targets.

1. Create a new history for this tutorial
2. Import the following datasets and choose the type of data as tabular

https://zenodo.org/api/files/efd372b1-4d11-4f43-bba6-66e75a0b4d15/test_data.tabular
https://zenodo.org/api/files/efd372b1-4d11-4f43-bba6-66e75a0b4d15/test_target.tabular
https://zenodo.org/api/files/efd372b1-4d11-4f43-bba6-66e75a0b4d15/train_data.tabular

• 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

By default, Galaxy uses the URL as the name, so rename the files with a more useful name.

3. Rename datasets to train_data, test_data and test_target

### tip Tip: Renaming a dataset

• 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

## Learn from training data

Gradient boosting regressor is used for this task. It is an ensemble based regressor consisting of weak learners (e.g. decision trees). It learns features from training dataset (train_data) and maps all rows to respective targets which are real numbers. The process of mapping gives a trained model.

### hands_on Hands-on: Train a model

1. Ensemble methods for classification and regression tool with the following parameters to train the regressor:
• “Select a Classification Task”: Train a model
• “Select an ensemble method”: Gradient Boosting Regressor
• “Select input type”: tabular data
• param-file “Training samples dataset”: train_data
• param-check “Does the dataset contain header”: Yes
• param-select “Choose how to select data by column”: All columns BUT by column header name(s)
• param-text “Type header name(s)”: target
• param-file “Dataset containing class labels”: train_data
• param-check “Does the dataset contain header”: Yes
• param-select “Choose how to select data by column”: Select columns by column header name(s)
• param-text “Select target column(s)”: target
2. Rename the generated file to model

## Predict using test data

Similar to the classification task, the trained model is evaluated on test_data which predicts a target value for each row and the predicted targets are compared to the expected targets.

### hands_on Hands-on: Predict categories using the model

1. Ensemble methods for classification and regression tool with the following parameters to predict targets of test data using the trained model:
• “Select a Classification Task”: Load a model and predict
• param-file “Models”: model
• param-file “Data (tabular)”: test_data
• param-check “Does the dataset contain header”: Yes
• param-select “Select the type of prediction”: Predict class labels
2. Rename the generated file to predicted_data

## Visualise the prediction

We will evaluate the predictions by comparing them to the expected targets.

### hands_on Hands-on: Check and visualize the predictions

1. Plot actual vs predicted curves and residual plots tool with the following parameters to visualise the predictions:
• param-file “Select input data file”: test_target
• param-file “Select predicted data file”: predicted_data

The last tool creates the following plots:

1. True vs predicted targets curves:

In figure 6 the corresponding points in both these curves should be close to each other for a good regression performance.

2. Scatter plot for true vs. predicted targets:

Figure 7 shows the performance of the regression task. The data points (blue) lie along the orange curve (y = x) which shows that the true and predicted values are close. More the number of points are aligned along the x = y line, better is the prediction. R2 score is close to the best possible score of 1.0.

3. Residual plot between residual (predicted - true) and predicted targets:

Figure 8 shows a random pattern of points. For a good regression performance, this plot should exhibit a random pattern.

By following these steps, we learn how to perform regression and visualise the predictions using Galaxy machine learning and plotting tools. The features of the training data are mapped to the real-valued targets. This mapping is used to make predictions on an unseen (test) dataset. The quality of predictions is visualised using a plotting tool.

# Conclusion

We learned how to perform classification and regression using different datasets and machine learning tools in Galaxy and visualized the output in multiple plots. There are many other classifiers and regressors in the Galaxy machine learning suite which can be tried out on these datasets to find how they perform. Different datasets can also be analysed using these classifiers and regressors.

### keypoints Key points

• There is two types of machine learning's supervised approaches, classification and regression.
• In supervised approaches, the target for each sample is known.
• For classification and regression tasks, data is divided into training and test sets.
• Using classification, the categories of rows are learned using the training set and predicted using the test set.
• Using regression, real-valued targets are learned using the training set and predicted using the test set.