Statistical Analysis and Modeling in R: Performing Classification
R Programming 4.0+
| Expert
- 13 Videos | 1h 36m 32s
- 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
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discover the key concepts covered in this courserecall the key metrics to evaluate classifiersfit and interpret the S-curve of logistic regressiontrain and evaluate a logistic regression modeltrain and evaluate a logistic model using all predictorstrain a model on an imbalanced datasetinterpret the significance of coefficients, confidence intervals, and odds ratios
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evaluate a model built using an imbalanced datasetuse resampling techniques to improve the modelrecall the basic structure of decision tree modelsexplore and pre-process data before model fittinguse decision tree models for predictionsummarize the key concepts covered in this course
IN THIS COURSE
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1.Course Overview2m 9sUP NEXT
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2.Recognizing and Evaluating Classification Models8m 21s
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3.Interpreting Logistic Regression Using R8m 12s
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4.Training and Evaluating a Logistic Regression Model10m 12s
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5.Building a Logistic Model in R Using all Predictors6m 23s
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6.Using R to Train a Model with Imbalanced Data8m 31s
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7.Building and Evaluating Models with R7m 9s
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8.Using R to Evaluate Imbalanced Data Model Types6m 27s
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9.Using Resampling Techniques on Imbalanced Data in R10m 35s
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10.Recognizing Decision Tree Models8m 1s
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11.Using R to Explore and Process Data6m 56s
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12.Visualizing Decision Trees and Performing Prediction11m 11s
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13.Course Summary2m 25s
EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE
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