2.6 Traditional ML packages in code

Key points

1. Hyperparameters are the settings you tune to get the best model. NVIDIA's scikit-learn glossary says grid search automates this by testing various configurations, and is used with cross-validation to evaluate models.

   What NVIDIA says (2)

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

   — What is scikit-learn?

   “Grid search further automates hyperparameter optimization, testing various configurations for improved accuracy.”

   — What is scikit-learn?

2. TF-IDF (term frequency–inverse document frequency) is a way to vectorize text, that is, turn it into numbers. NVIDIA's RAPIDS text post describes Count and TF-IDF vectorizers in cuML that can scale across multiple graphics processing units (GPUs) and machines.

   What NVIDIA says (3)

   “subpackage in cuML by adding Count and TF-IDF vectorizer”

   — NLP and Text Processing with RAPIDS: Now Simpler and Faster

   “You can also scale your TF-IDF workflow to multiple GPUs and machines using cuml”

   — NLP and Text Processing with RAPIDS: Now Simpler and Faster

   “by first vectorizing them using TF-IDF”

   — NLP and Text Processing with RAPIDS: Now Simpler and Faster

3. NVIDIA's pandas glossary lists data manipulation and cleaning operations such as selecting a subset, derived columns, sorting, joining, filling, replacing, summary statistics and plotting.

   What NVIDIA says (1)

   “pandas also allows for various data manipulation operations and data cleaning features, including selecting a subset, creating derived columns, sorting, joining, filling, replacing, summary statistics, and plotting.”

   — What Is Pandas and Why Does it Matter?

Key terms

• cuML: RAPIDS GPU library for traditional machine learning, including text vectorizers.
• scikit-learn: Python library for traditional machine learning.
• TF-IDF: A way to turn text into numeric feature vectors.
• Grid search: Trying many hyperparameter combinations to find the best one.

Sample question

What does scikit-learn's grid search do?