training data

USAGE SUMMARY

"training data" is a perfectly acceptable and commonly used phrase in written English.
You can use it to refer to a dataset of information used to aid a computer or machine in performing a specific task or function. For example, "We used training data to train a machine learning algorithm to detect objects in an image."

✓ Grammatically correct

Academia

News & Media

Science

Human-verified examples from authoritative sources

Exact Expressions

59 human-written examples

Modeling scientist: Models, training data, algorithms.

Harvard Business Review

It is people who provide the training data.

The Guardian

But other aptitudes require training, data, experience and practice.

The Guardian - Opinion

Firstly, you can create unlimited training data.

Massachusetts Institute of Technology

As such, the training data is ambiguous.

Stanford University

So again, this requires label training data.

Massachusetts Institute of Technology

Activity test results were used as training data for NN.

Catalysis Today

Prepare the training data that machine learning will operate on.

Harvard Business Review

As of 12/1/2015: New training data tables.

University of California, Berkeley

AGI involves algorithms that learn even without training data.

Harvard University

Human-verified similar examples from authoritative sources

Similar Expressions

1 human-written examples

Learning from Training Data Set.

EURASIP Journal on Image and Video Processing

Expert writing Tips

Best practice

When preparing "training data", ensure it accurately represents the real-world scenarios your model will encounter to avoid bias and improve performance.

Common error

A common mistake is to overlook imbalanced classes within the "training data". This can lead to a model that is biased towards the majority class and performs poorly on the minority class. Always balance your data or use techniques like oversampling or undersampling to mitigate this issue.

Linguistic Context

The phrase "training data" functions as a noun phrase, referring to the data used to train a model in machine learning. Ludwig AI indicates that this phrase is grammatically correct and commonly used.

Expression frequency: Very common

Frequent in

Science

36%

Academia

27%

News & Media

19%

Less common in

Formal & Business

10%

Encyclopedias

0%

Wiki

0%

Ludwig's WRAP-UP

In summary, "training data" is a noun phrase widely used in the fields of machine learning, artificial intelligence, and data science. It refers to the dataset used to train a model. Ludwig AI confirms that it is grammatically sound and frequently employed in both academic and professional settings. The phrase appears most often in scientific and academic contexts, indicating its importance in technical discussions. When using "training data", ensure it is representative and of high quality to achieve optimal model performance. Consider alternatives like "training dataset" or "learning data" to add variety to your writing.

More alternative expressions

Phrases that express similar concepts, ordered by semantic similarity:

Training dataset

Replaces the noun "data" with "dataset", emphasizing a structured collection of data.

Learning data

Focuses on the purpose of the data, which is for learning.

Model training data

Specifies the data's use for model training.

Supervised learning data

Highlights the type of learning involved.

Labeled data

Emphasizes the presence of labels for supervised learning.

Input data for training

Rephrases to clarify that the data serves as input.

Data for model training

Reorders the phrase for emphasis.

Examples for training

Uses "examples" to represent the training data.

Training samples

Refers to the individual data points used for training.

Ground truth data

Highlights the correctness and reliability of the training data.

FAQs

What is "training data" used for in machine learning?

"Training data" is used to train machine learning models, allowing them to learn patterns and make predictions on new, unseen data. It's a critical component in supervised learning algorithms.

How do I collect good "training data"?

Collecting good "training data" involves gathering a representative sample of data relevant to the problem you're trying to solve. Ensure the data is accurate, labeled correctly, and covers a wide range of scenarios. You can also consider using data augmentation techniques to expand your dataset.

What can I say instead of "training data"?

You can use alternatives like "training dataset", "learning data", or "model training data" depending on the specific context.

Why is the quality of "training data" important?

The quality of "training data" directly impacts the performance of your machine learning model. Poor quality data, such as inaccurate labels or biased samples, can lead to a model that performs poorly or makes incorrect predictions.