Evaluate a Binary Classifier

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prerequisites
basic Python • basic pandas • basic NumPy • basic Matplotlib • basic seaborn • basic scikit-learn • basic Jupyter Notebook • basics of machine learning • basics of exploratory analysis
skills learned
evaluate predictions and accuracy • visualize patterns with classes • conduct hyperparameter tuning • prepare a model for production deployment
Harshit Tyagi
1 week · 3-5 hours per week · INTERMEDIATE
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catalog / Data Science / Machine Learning
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Look inside
Judging the effectiveness of a machine learning model requires in-depth analysis. This quick liveProject builds on the work you have completed in Machine Learning for Classification. You’ll assess your early models and consider better alternatives. You’ll plot the ROC curves of the model and compare it to multiple dummy models, and tune your hyperparameters to deliver the most accurate results possible.
This project is designed for learning purposes and is not a complete, production-ready application or solution.

project author

Harshit Tyagi
Harshit Tyagi has helped over a thousand students master the fundamentals of programming and data science. In his roles at OpenClassrooms and Coding Ninjas, he leverages his technical expertise to conduct workshops and help students bring their course projects to the finish line. He also has a YouTube channel, where he covers fundamental concepts in data science and Python, interview tips, and more. In addition to focusing on data science education, Harshit has developed data processing algorithms with research scientists at Yale, MIT and UCLA.

prerequisites

This liveProject is for Python data scientists who want to expand their capabilities in evaluating and tuning machine learning models. To begin this liveProject you will need to be familiar with:

TOOLS
  • Basic Python
  • Basic pandas
  • Basic NumPy
  • Basic Matplotlib
  • Basic seaborn
  • Basic scikit-learn
  • Basic Jupyter Notebook
TECHNIQUES
  • Basics of machine learning
  • Basics of exploratory analysis

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