computer science

Scikit-multiflow

Scikit-multiflow is a computer science topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Scikit-multiflow rather than just read about it. In short: scikit-mutliflow (also known as skmultiflow) is a free and open source software machine learning library for multi-output/multi-label and stream data written in Python. Overview scikit-multiflow allows to easily design and run experiments and to extend existing stream learning algorithms.

Scikit-multiflow — illustration

Key takeaways

Scikit-multiflow belongs to computer science; place it in that map before memorising details.
Learn the definition first, then one example that makes the definition concrete.
Connect Scikit-multiflow to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of Scikit-multiflow from memory before moving on to harder problems.

Reference excerpt

scikit-mutliflow (also known as skmultiflow) is a free and open source software machine learning library for multi-output/multi-label and stream data written in Python.

Overview scikit-multiflow allows to easily design and run experiments and to extend existing stream learning algorithms. It features a collection of classification, regression, concept drift detection and anomaly detection algorithms. It also includes a set of data stream generators and evaluators. scikit-multiflow is designed to interoperate with Python's numerical and scientific libraries NumPy and SciPy and is compatible with Jupyter Notebooks.

Implementation The scikit-multiflow library is implemented under the open research principles and is currently distributed under the BSD 3-clause license. scikit-multiflow is mainly written in Python, and some core elements are written in Cython for performance. scikit-multiflow integrates with other Python libraries such as Matplotlib for plotting, scikit-learn for incremental learning methods compatible with the stream learning setting, Pandas for data manipulation, Numpy and SciPy.

Components The scikit-multiflow is composed of the following sub-packages:

anomaly_detection: anomaly detection methods. data: data stream methods including methods for batch-to-stream conversion and generators. drift_detection: methods for concept drift detection. evaluation: evaluation methods for stream learning. lazy: methods in which generalisation of the training data is delayed until a query is received, i.e., neighbours-based methods such as kNN. meta: meta learning (also known as ensemble) methods. neural_networks: methods based on neural networks. prototype: prototype-based learning methods. rules: rule-based learning methods. transform: perform data transformations. trees: tree-based methods, e.g. Hoeffding trees which are a type of decision tree for data streams.

History scikit-multiflow started as a collaboration between researchers at Télécom Paris (Institut Polytechnique de Paris) and École Polytechnique. Development is currently carried by the University of Waikato, Télécom Paris, École Polytechnique and the open research community. In late 2020, the development teams of scikit-multiflow and creme (another prominent online learning library) decided to join forces and create the River library. Development on the two projects has stopped in favor of River.

Illustrations

Worked examples

Example 1 — a first encounter with Scikit-multiflow

Start with the simplest possible case. Write down what Scikit-multiflow claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In computer science, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Scikit-multiflow before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Scikit-multiflow ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Scikit-multiflow

In research: Scikit-multiflow appears in computer science research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry: Engineering practice reuses Scikit-multiflow in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom: Scikit-multiflow is common in secondary-school and first-year university syllabi. It links to neighbouring topics Data mining and machine learning software, Free software programmed in Python, Free statistical software, so understanding it makes those chapters shorter.
In everyday life: Look for Scikit-multiflow outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study Scikit-multiflow in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Scikit-multiflow means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain Scikit-multiflow out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Scikit-multiflow in simple terms?

scikit-mutliflow (also known as skmultiflow) is a free and open source software machine learning library for multi-output/multi-label and stream data written in Python. Overview scikit-multiflow allows to easily design and run experiments and to extend existing stream learning algorithms.

Why does Scikit-multiflow matter?

Because it connects several computer science ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Scikit-multiflow?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Scikit-multiflow.