Machine Learning Foundation: Model Training is Where the Machine Learns in Machine Learning Pipeline Phases

Discover which phase of the ML pipeline the machine actually learns from data. Understand the key steps of model training, deployment, feature engineering and data processing.

Question

In which phase of the ML pipeline does the machine learn from the data?

A. Model training
B. Deployment
C. Feature engineering
D. Data processing

Answer

A. Model training

Explanation

In the machine learning pipeline, it is during the model training phase that the machine actually learns from the data.

The typical ML pipeline consists of several key phases:

1. Data processing: Raw data is collected, cleaned, and formatted to be suitable for training.
2. Feature engineering: The processed data is transformed into features that can be used by the ML algorithms.
3. Model training: The ML model is fed the engineered features and learns patterns and relationships in the training data. This is the phase where the actual “learning” happens, as the model iteratively updates its parameters to minimize a loss function.
4. Deployment: Once trained, the model is deployed into a production environment to make predictions on new, unseen data.

So in summary, while all the pipeline phases are important, it is specifically during model training that the machine learns from the provided data to build a predictive model. The other phases focus on preparing the data, creating useful features, and operationalizing the trained model.

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