machine learning models

USAGE SUMMARY

The phrase "machine learning models" is correct and usable in written English.
You can use it when discussing how computers use algorithms to learn from data in order to solve complex problems. For example, "The company is researching different machine learning models in order to identify patterns in customer purchases."

✓ Grammatically correct

Science

News & Media

Human-verified examples from authoritative sources

Exact Expressions

60 human-written examples

Atolla use two distinct machine learning models.

TechCrunch

However, traditional unit tests — the backbone of traditional software testing — don't really work with machine learning models, because the correct output of machine learning models isn't known beforehand.

TechCrunch

These data are to train the machine learning models to predict leak source locations.

Journal of Loss Prevention in the Process Industries

That is, traditional machine learning models — not deep neural networks — are powering most AI applications.

TechCrunch

Like most machine learning models, this one also became better as it gathered more data.

TechCrunch

For example, in 2015, it also outsourced FeatureFu, a toolkit for building machine learning models.

TechCrunch

Comet.ml allows data scientists and developers to easily monitor, compare and optimize their machine learning models.

TechCrunch

Developers don't think of machine learning models as Android- or iOS-specific, after all.

TechCrunch

Machine learning models train on examples taken from the real-world environment they represent.

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Today's machine learning models can be trained on data and used for classifying defined objects.

TechCrunch

You'll train your own machine learning models and use them in your apps quickly.

TechCrunch

Expert writing Tips

Best practice

Always consider the limitations and potential biases inherent in your "machine learning models".

Common error

Avoid using "machine learning models" as a catch-all term. Be specific about the model type (e.g., regression, classification) to provide more context and precision.

Linguistic Context

The phrase "machine learning models" functions as a noun phrase, typically serving as the subject or object of a sentence. It refers to algorithms and statistical models that enable computers to learn from data without explicit programming. As Ludwig AI points out, these models are crucial for solving complex problems.

Expression frequency: Very common

Frequent in

Science

55%

News & Media

40%

Formal & Business

5%

Less common in

Social Media

0%

Encyclopedias

0%

Wiki

0%

Ludwig's WRAP-UP

In summary, "machine learning models" is a widely used and grammatically correct phrase referring to a set of algorithms and statistical methods that enable computers to learn from data. As Ludwig AI confirms, its usage is appropriate in various contexts, primarily within the sciences and news media. When using the phrase, it's crucial to be specific about the model type for clarity and to acknowledge potential limitations. Related terms such as "AI models" or "predictive algorithms" may be suitable alternatives depending on the context. The consistent usage and authoritative sources associated with this phrase underscore its importance in contemporary discussions about artificial intelligence and data analysis.

More alternative expressions

Phrases that express similar concepts, ordered by semantic similarity:

ML models

Uses abbreviation of "machine learning".

AI models

Uses abbreviation of "artificial intelligence" instead of machine learning. Broader term.

predictive algorithms

Focuses on the predictive function of these models.

computational models for learning

Highlights the computational aspect of learning.

data-driven models

Emphasizes the role of data in creating these models.

statistical learning models

Highlights the statistical foundations.

algorithmic prediction systems

Highlights systems that use algorithms to make predictions.

pattern recognition systems

Highlights systems that recognize patterns in the data.

automated learning algorithms

Focuses on the automated nature of the learning process.

intelligent systems

A more general term encompassing systems with intelligent behavior.

FAQs

How are "machine learning models" used in practice?

"Machine learning models" are employed for prediction, classification, and pattern recognition across various fields like healthcare, finance, and marketing.

What are some alternatives to saying "machine learning models"?

Depending on the context, you can use alternatives like "predictive algorithms", "AI models", or "statistical learning models".

What's the difference between "machine learning models" and "AI models"?

"AI models" is a broader term that includes "machine learning models". While AI encompasses all techniques for creating intelligent machines, machine learning is a subset that focuses on models that learn from data.

How do I choose the right "machine learning models" for my data?

Selecting the appropriate "machine learning models" depends on the nature of your data, the type of problem you're trying to solve (e.g., classification, regression), and the desired level of accuracy.