Examples of 'feature engineering' in a sentence
Meaning of "feature engineering"
Feature engineering is the process of creating new features or transforming existing data features to improve the performance of a machine learning model. It involves selecting, combining, and transforming variables to extract useful information and make the data more suitable for modeling
How to use "feature engineering" in a sentence
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feature engineering
Feature engineering with thermophysical properties library.
We spent most of our efforts in feature engineering.
You can use feature engineering to simplify data.
One such process is called feature engineering.
Automation of feature engineering has become an emerging topic of research in academia.
Also known as data transformation or feature engineering.
This feature engineering process is important to obtain high quality solutions.
Great for exploration and feature engineering.
The need for manual feature engineering can be obviated by automated feature learning.
It involves data cleansing and feature engineering.
Feature engineering is an informal topic, but it is considered essential in applied machine learning.
Featuretools offers a solution for automatic feature engineering.
Hence the need for feature engineering still remains.
The next step is to prepare the data through feature engineering.
Feature engineering is the science ( and art ) of extracting more information from existing data.
See also
So now it is time to talk about the feature engineering.
Feature engineering is a hard problem to automate, however, and not all AutoML systems handle it.
Identifying the relevant features for your model is called feature engineering.
Another large part of Data Science known as feature engineering has undergone significant disruption.
ML practitioners can then focus more on modelling and less on feature engineering.
We develop feature engineering techniques that transform IP flows to device-level data points.
Next, you will be introduced to building models, including feature engineering and constructing models.
During experiments, the model performed as precisely as advanced models that need manual feature engineering.
Next, Blumenstock used a two-step procedure common in machine learning, feature engineering followed by supervised learning.
Without this combination, these models perform significantly worse than methods based on linguistic feature engineering.
Mastercourse Data Innovation, The art of feature engineering.
After Target hits a critical mass of data, it performs feature engineering.
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Examples of using Engineering
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Models formulae engineering designs and specifications
Claims that foods are products of genetic engineering
Engineering and architecture projects focused on the
