Machine Learning – Classification

Learn in this article about Machine Learning and the classification of algorithms, written by one of our software engineering experts from the HAT.

machine learning 3

Algorithms’ classification predict the class or category for a single instance of data. For example, email filters use binary classification to determine if an email is spam. There are two forms of classification tasks. The first is binary classification, where the goal is to predict one of two outcomes.

The other is multiclass classification, where the goal is to predict one of many outcomes. The output of a classification algorithm is called a classifier, which can be used to predict the label of a new (unlabeled) instance.

This is a supervised learning algorithms make predictions based on a set of examples. For instance, historical stock prices can be used to hazard guesses at future prices. Each example used for training is labeled with the value of interest.

Let’s start with the question: Is this A or B?

This family of algorithms is called two-class classification.
It’s useful for any question that has just two possible answers. They are several algorithms for use for this question. This next image represent a two-classes support vector machine, one of the most popular used.

Bespoke software development


Is this A or B or C or D, etc.?

This is called multiclass classification and it’s useful when you have several—or several thousand—possible answers. Multiclass classification chooses the most likely one.
The next image represent a one vs. all classification.

Software engineering


AzureML algorithms for Classification

The category Initialize Classification Model includes the following modules:
To see the complete documentation of each one go here!

Tech partners

Tech partners



1- Selection of data set
We use the Adult Census Incoming Binary classification data set.
The column income is the label.

Software engineering
2- Using select column and Split data
We select the columns that we know will be more useful for the prediction, and then we Split the data for train and score the model.

Tech partners
3- Using the Two-class Boosted Decision
We drop the classification model into the canvas and leave the default parameters values.

Bespoke software
4- Score and evaluate model
Run the experiment to check the score and the evaluate model results.
The right two columns, Scored Labels and Scored Probabilities are the prediction results. The Scored Probabilities column shows the probability that the predicted class belongs to the positive one (in this case “> 50K”).

software engineering

Machine Learning

To see more documentation for interpret models result see here.

5- Finished experiment
The image below represent the entire experiment ready to run.

Machine Learning

Share this articleShare on LinkedInTweet about this on TwitterShare on FacebookShare on Google+Email this to someone
Go Back