Statistical Analysis and Modeling in R: Performing Classification

R Programming 4.0+
  • 13 Videos | 1h 37m 11s
  • 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 12s
    UP NEXT
  • Playable
    2. 
    Recognizing and Evaluating Classification Models
    8m 23s
  • Locked
    3. 
    Interpreting Logistic Regression Using R
    8m 15s
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    4. 
    Training and Evaluating a Logistic Regression Model
    10m 15s
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    5. 
    Building a Logistic Model in R Using all Predictors
    6m 26s
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    6. 
    Using R to Train a Model with Imbalanced Data
    8m 34s
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    7. 
    Building and Evaluating Models with R
    7m 12s
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    8. 
    Using R to Evaluate Imbalanced Data Model Types
    6m 30s
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    9. 
    Using Resampling Techniques on Imbalanced Data in R
    10m 38s
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    10. 
    Recognizing Decision Tree Models
    8m 4s
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    11. 
    Using R to Explore and Process Data
    6m 59s
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    12. 
    Visualizing Decision Trees and Performing Prediction
    11m 14s
  • Locked
    13. 
    Course Summary
    2m 28s

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