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NOT : 2019SACLS470Apprentissage statistics a part DE
variables categories nonuniformisees
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How to fill out statistical learning with high-cardinality
How to fill out statistical learning with high-cardinality
01
Identify the high-cardinality variables in your dataset.
02
Consider using techniques like one-hot encoding or target encoding to handle high-cardinality variables.
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Normalize or standardize features to ensure each variable contributes equally to the model.
04
Evaluate model performance and adjust as needed.
Who needs statistical learning with high-cardinality?
01
Data scientists who are working with datasets that contain variables with a large number of unique values.
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Researchers looking to build predictive models using data with high-cardinality features.
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What is statistical learning with high-cardinality?
Statistical learning with high-cardinality is a method used in data analysis to handle datasets with a large number of unique values or categories.
Who is required to file statistical learning with high-cardinality?
Researchers, data scientists, and analysts working with datasets containing high-cardinality variables are required to utilize statistical learning techniques.
How to fill out statistical learning with high-cardinality?
Statistical learning with high-cardinality can be approached using algorithms like random forests, gradient boosting machines, or deep learning methods to effectively analyze the data.
What is the purpose of statistical learning with high-cardinality?
The purpose of statistical learning with high-cardinality is to accurately model and make predictions based on datasets with a large number of unique values or categories.
What information must be reported on statistical learning with high-cardinality?
The analysis methodology, variable selection process, model performance metrics, and any insights derived from the high-cardinality dataset must be reported.
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