SOTAVerified

Feature Engineering

Feature engineering is the process of taking a dataset and constructing explanatory variables — features — that can be used to train a machine learning model for a prediction problem. Often, data is spread across multiple tables and must be gathered into a single table with rows containing the observations and features in the columns.

The traditional approach to feature engineering is to build features one at a time using domain knowledge, a tedious, time-consuming, and error-prone process known as manual feature engineering. The code for manual feature engineering is problem-dependent and must be re-written for each new dataset.

Papers

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Showing 1171–1180 of 1706 papers

TitleStatusHype
Transferable Deep Learning Power System Short-Term Voltage Stability Assessment with Physics-Informed Topological Feature Engineering—0
Transfer Learning from an Auxiliary Discriminative Task for Unsupervised Anomaly Detection—0
Transfer Learning in Conversational Analysis through Reusing Preprocessing Data as Supervisors—0
Transformers Beyond Order: A Chaos-Markov-Gaussian Framework for Short-Term Sentiment Forecasting of Any Financial OHLC timeseries Data—0
Transforming Podcast Preview Generation: From Expert Models to LLM-Based Systems—0
Transform then Explore: a Simple and Effective Technique for Exploratory Combinatorial Optimization with Reinforcement Learning—0
Transitional Uncertainty with Layered Intermediate Predictions—0
Transition-based Dependency DAG Parsing Using Dynamic Oracles—0
Transition-based Dependency Parsing Using Two Heterogeneous Gated Recursive Neural Networks—0
Transparent text quality assessment with convolutional neural networks—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified