Identifying Good Features for Machine Learning
Identifying Good Features for Machine Learning Data Science Project
Introduction to Supervised Learning with scikit-learn

Identifying Good Features for Machine Learning

The goal of this project is to explore various techniques and strategies to identify the most relevant and informative features for a given dataset.
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Project Activities

All our Data Science projects include bite-sized activities to test your knowledge and practice in an environment with constant feedback.

All our activities include solutions with explanations on how they work and why we chose them.

multiplechoice

True or False: Feature selection is the process of randomly choosing any subset of features from the dataset.

multiplechoice

True or False: Dimensionality reduction is a technique used in feature engineering to increase the number of features in the dataset.

multiplechoice

True or False: Feature engineering involves creating new features by combining or transforming existing ones to enhance the model's performance.

multiplechoice

True or False: Features that exhibit high discriminative power are typically highly correlated with each other.

Identifying Good Features for Machine LearningIdentifying Good Features for Machine Learning
Author

Verónica Barraza

This project is part of

Introduction to Supervised Learning with scikit-learn

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