Statistical Analysis and Modeling in R: Performing Classification

R Programming 4.0+    |    Expert
  • 13 Videos | 1h 42m 2s
  • Includes Assessment
  • Earns a Badge
Classification models are used to classify or categorize data points into two or more categories. Learn how these models work and how you can evaluate your classification models using the confusion matrix and metrics such as accuracy, precision, and recall. During this course, you'll perform classification using both logistic regression and an imbalanced dataset. You'll also examine why precision or recall scores may be better metrics than accuracy to evaluate such models. Furthermore, build a classification model using decision trees, visualize the tree structure, and explore the variable importance assigned by this tree structure to understand and interpret the model. When you've finished this course, you'll be able to confidently use logistic regression and decision trees to build classification models and evaluate your models using accuracy, precision, and recall.

WHAT YOU WILL LEARN

  • discover the key concepts covered in this course
    recall the key metrics to evaluate classifiers
    fit and interpret the S-curve of logistic regression
    train and evaluate a logistic regression model
    train and evaluate a logistic model using all predictors
    train a model on an imbalanced dataset
    interpret the significance of coefficients, confidence intervals, and odds ratios
  • evaluate a model built using an imbalanced dataset
    use resampling techniques to improve the model
    recall the basic structure of decision tree models
    explore and pre-process data before model fitting
    use decision tree models for prediction
    summarize the key concepts covered in this course

IN THIS COURSE

  • Playable
    1. 
    Course Overview
    2m 9s
    UP NEXT
  • Playable
    2. 
    Recognizing and Evaluating Classification Models
    8m 21s
  • Locked
    3. 
    Interpreting Logistic Regression Using R
    8m 12s
    FREE ACCESS
  • Locked
    4. 
    Training and Evaluating a Logistic Regression Model
    10m 12s
    FREE ACCESS
  • Locked
    5. 
    Building a Logistic Model in R Using all Predictors
    6m 23s
    FREE ACCESS
  • Locked
    6. 
    Using R to Train a Model with Imbalanced Data
    8m 31s
    FREE ACCESS
  • Locked
    7. 
    Building and Evaluating Models with R
    7m 9s
    FREE ACCESS
  • Locked
    8. 
    Using R to Evaluate Imbalanced Data Model Types
    6m 27s
    FREE ACCESS
  • Locked
    9. 
    Using Resampling Techniques on Imbalanced Data in R
    10m 35s
    FREE ACCESS
  • Locked
    10. 
    Recognizing Decision Tree Models
    8m 1s
    FREE ACCESS
  • Locked
    11. 
    Using R to Explore and Process Data
    6m 56s
    FREE ACCESS
  • Locked
    12. 
    Visualizing Decision Trees and Performing Prediction
    11m 11s
    FREE ACCESS
  • Locked
    13. 
    Course Summary
    2m 25s
    FREE ACCESS

EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE

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