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Train (Training) Set

What is a train set?

‍The train (or training) set is the portion of the training data that is used to train a machine learning model. It typically consists of 70-85% of the total available training data. It is essential to ensure that the training set is representative of the problem domain and includes a diverse set of examples. To maintain the integrity of the training process and avoid overfitting, it is essential to keep the training set separate from the validation and test sets. These other sets are used to fine-tune the model's hyperparameters and evaluate its performance on unseen data, respectively.

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AI Lakehouse
AI Pipeline
Auto-regressive Models
AutoML
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Backfill features
Backfill training data
Backpressure for feature stores
Batch Inference Pipeline
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CI/CD for MLOps
Compound AI Systems
Context Window for LLMs
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DAG Processing Model
Data Compatibility
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Data Lakehouse
Data Leakage
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Data-Centric ML
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ELT
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Feature
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Generative AI
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Hallucinations in LLMs
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Idempotent Machine Learning Pipelines
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LLM Code Interpreter
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KServe
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RLHF - Reinforcement Learning from Human Feedback
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Retrieval Augmented Generation (RAG) for LLMs
RoPE Scaling
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SQL UDF in Python
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Splitting Training Data
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Test Set
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Upstream
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Validation Set
Vector Database
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