Evaluate Machine Learning Models with Yellowbrick

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このガイド付きプロジェクトでは、次のことを行います。

Build and evaluate a logistic regression classifier with scikit-learn

Use visualization and model diagnostic tools from Yellowbrick to steer your machine learning workflow

Clock2 hours
Intermediate中級
Cloudダウンロード不要
Video分割画面ビデオ
Comment Dots英語
Laptopデスクトップのみ

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.

あなたが開発するスキル

  • Data Science
  • Machine Learning
  • Python Programming
  • Data Visualization (DataViz)
  • Scikit-Learn

ステップバイステップで学習します

ワークエリアを使用した分割画面で再生するビデオでは、講師がこれらの手順を説明します。

  1. ROC/AUC Plots

  2. Classification Report and Confusion Matrix

  3. Cross-validation Scores

  4. Evaluating Class Balance

  5. Discrimination Threshold for Logistic Regression

ガイド付きプロジェクトの仕組み

ワークスペースは、ブラウザに完全にロードされたクラウドデスクトップですので、ダウンロードは不要です

分割画面のビデオで、講師が手順ごとにガイドします

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