2.3 Machine learning fundamentals

Key points

1. Here a transformer is a scikit-learn preprocessing step, not a neural network. Feature engineering means shaping raw data into useful inputs, such as scaled numbers.

   What NVIDIA says (2)

   “Transformers apply algorithms to clean or reshape the training data before it is fed into a model.”

   — What is scikit-learn?

   “For example, feature scaling or encoding categorical variables prepares the subset of input data for optimal performance.”

   — What is scikit-learn?

2. A hyperparameter is a setting chosen before training, such as tree depth. Grid search tries many settings. Cross-validation scores each fairly on several splits.

   What NVIDIA says (1)

   “For effective model selection, scikit-learn incorporates tools like grid search and cross-validation to identify the best hyperparameters and evaluate model performance.”

   — What is scikit-learn?

3. A label is the known answer for a training example. Clustering is a common unsupervised task.

   What NVIDIA says (1)

   “Supervised learning algorithms use labeled data, unsupervised learning algorithms find patterns in unlabeled data.”

   — What is Machine Learning and Why Does It Matter?

Key terms

• Cross-validation: Training and validating several times on different splits of the data to estimate accuracy fairly.
• scikit-learn: A Python library for traditional machine learning built on NumPy.
• Feature engineering: Shaping raw data into useful model inputs, for example by scaling or encoding.
• Hyperparameter: A setting chosen before training, such as learning rate or LoRA rank.

Sample question

In scikit-learn, what do transformers do before data reaches the model?