Evaluate Machine Learning Models with Yellowbrick
Build and evaluate a logistic regression classifier with scikit-learn
Use visualization and model diagnostic tools from Yellowbrick to steer your machine learning workflow
Welcome to this project-based course on Evaluating Machine Learning Models with Yellowbrick. In this course, we are going to use visualizations to steer our machine learning workflow. The problem we will tackle is to predict whether rooms in apartments are occupied or unoccupied based on passive sensor data such as temperature, humidity, light and CO2 levels. We will build a logistic regression model for binary classification. This is a continuation of the course on Room Occupancy Detection. With an emphasis on visual steering of our analysis, we will cover the following topics in our machine learning workflow: model evaluation with ROC/AUC plots, confusion matrices, cross-validation scores, and setting discrimination thresholds for logistic regression models. This course runs on Coursera's hands-on project platform called Rhyme. On Rhyme, you do projects in a hands-on manner in your browser. You will get instant access to pre-configured cloud desktops containing all of the software and data you need for the project. Everything is already set up directly in your internet browser so you can just focus on learning. For this project, you’ll get instant access to a cloud desktop with Python, Jupyter, Yellowbrick, and scikit-learn pre-installed. Notes: - You will be able to access the cloud desktop 5 times. However, you will be able to access instructions videos as many times as you want. - This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
Habilidades que desarrollarás
Aprende paso a paso
En un video que se reproduce en una pantalla dividida con tu área de trabajo, tu instructor te guiará en cada paso:
ROC/AUC Plots
Classification Report and Confusion Matrix
Cross-validation Scores
Evaluating Class Balance
Discrimination Threshold for Logistic Regression
Cómo funcionan los proyectos guiados
Tu espacio de trabajo es un escritorio virtual directamente en tu navegador, no requiere descarga.
En un video de pantalla dividida, tu instructor te guía paso a paso
Reseñas
Principales reseñas sobre EVALUATE MACHINE LEARNING MODELS WITH YELLOWBRICK
It was great overall. But you need to fix the audio issues in some of the tasks.
Preguntas Frecuentes
Preguntas Frecuentes
¿Qué obtendré si compro un Proyecto guiado?
¿Hay Proyectos guiados disponibles para computadoras de escritorio y dispositivos móviles?
¿Quiénes son los instructores de Proyectos guiados?
¿Puedo descargar el trabajo de mi Proyecto guiado después de completarlo?
¿Cuál es la política de reembolsos?
¿Hay ayuda económica disponible?
¿Puedo participar como oyente de un Proyecto guiado y mirar el video de forma gratuita?
¿Cuánta experiencia preciso tener para este Proyecto guiado?
¿Puedo completar este Proyecto guiado directamente utilizando mi navegador web, en lugar de instalar un software especial?
¿Cómo es la experiencia de aprendizaje con los Proyectos guiados?
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