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Setting up the validation framework — Machine Learning Zoomcamp Module 2

In general, the dataset is splitted into three parts: training, validation, and test. For each partition, we need to obtain feature matrices (X) and vectors of targets (y). First, the size of the partitions is calculated. Next, the records...

Machine Learning Zoomcamp › Module 2: Machine Learning for Regression › Setting up the validation framework

Notes

In general, the dataset is splitted into three parts: training, validation, and test. For each partition, we need to obtain feature matrices (X) and vectors of targets (y). First, the size of the partitions is calculated. Next, the records are shuffled to ensure that the values in the three partitions contain non-sequential records from the dataset. Finally, the partitions are created using the shuffled indices.

Pandas attributes and methods:

• df.iloc[] -> return subsets of records of a dataframe, being selected by numerical indices
• df.reset_index() -> restate the orginal indices
• del df[col] -> eliminate a column variable

Numpy methods:

• np.arange() -> return an array of numbers
• np.random.shuffle() -> return a shuffled array
• np.random.seed() -> set a seed for reproducibility

The entire code of this project is available in this jupyter notebook.

• Notes from Peter Ernicke

Key concepts

• Pandas
• Jupyter Notebooks

Related notes

• mlz-m01-introduction-to-numpy
• mlz-m01-introduction-to-pandas
• mlz-m01-linear-algebra-refresher
• mlz-m01-setting-up-the-environment
• mlz-m01-summary
• mlz-m02-baseline-model-for-car-price-prediction-project
• mlz-m02-car-price-prediction-project
• mlz-m02-car-price-prediction-project-summary
• mlz-m02-categorical-variables
• mlz-m02-computing-rmse-on-validation-data
• mlz-m02-data-preparation
• mlz-m02-exploratory-data-analysis
• mlz-m02-explore-more
• mlz-m02-feature-engineering
• mlz-m02-linear-regression
• mlz-m02-linear-regression-vector-form
• mlz-m02-regularization
• mlz-m02-root-mean-squared-error-rmse
• mlz-m02-training-linear-regression-normal-equation
• mlz-m02-tuning-the-model
• mlz-m02-using-the-model
• mlz-m03-data-preparation
• mlz-m03-eda
• mlz-m03-feature-importance-churn-rate-and-risk-ratio
• mlz-m03-feature-importance-correlation
• mlz-m03-feature-importance-mutual-information
• mlz-m03-logistic-regression
• mlz-m03-model-interpretation
• mlz-m03-one-hot-encoding
• mlz-m03-setting-up-the-validation-framework
• mlz-m03-training-logistic-regression-with-scikit-learn
• mlz-m04-accuracy-and-dummy-model
• mlz-m04-confusion-table
• mlz-m04-cross-validation
• mlz-m04-roc-auc
• mlz-m04-roc-curves
• mlz-m04-summary
• mlz-m06-data-cleaning-and-preparation
• mlz-m06-decision-trees-parameter-tuning
• mlz-m06-gradient-boosting-and-xgboost
• mlz-m06-xgboost-parameter-tuning
• mlz-m08-installation-of-tensorflow
• mlz-m10-creating-a-pre-processing-service

Sources

• Video
• Lesson file