https://github.com/trilokida/classification_metrics

https://github.com/trilokida/classification_metrics

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• Host: GitHub
• URL: https://github.com/trilokida/classification_metrics
• Owner: TrilokiDA
• Created: 2018-08-29T14:30:51.000Z (about 6 years ago)
• Default Branch: master
• Last Pushed: 2018-08-29T15:01:31.000Z (about 6 years ago)
• Last Synced: 2023-08-16T20:51:26.741Z (over 1 year ago)
• Language: Jupyter Notebook
• Size: 35.2 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Classification_metrics

**Dataset** can be download from https://www.kaggle.com/uciml/pima-indians-diabetes-database

* *Pregnancies:- Number of times pregnant*

* *Glucose:- Plasma glucose concentration a 2 hours in an oral glucose tolerance test*

* *BloodPressure:- Diastolic blood pressure (mm Hg)*

* *SkinThickness:- Triceps skin fold thickness (mm)*

* *Insulin:- 2-Hour serum insulin (mu U/ml)*

* *BMI:- Body mass index (weight in kg/(height in m)^2)*

* *DiabetesPedigreeFunction:- Diabetes pedigree function*

* *Age:- Age (years)*

* *Outcome:- Class variable (0 or 1)*

## Highlight some basic points with code like:

* Classification accuracy: percentage of correct predictions

* Null accuracy

* Comparing the true and predicted response values

* **Confusion matrix**

* **Metrics computed from a confusion matrix**

  * **Classification Accuracy:** Overall, how often is the classifier correct

  * **Classification Error:** Overall, how often is the classifier incorrect?

  * **Sensitivity:** When the actual value is positive, how often is the prediction correct?

  * **Specificity:** When the actual value is negative, how often is the prediction correct?

  * **False Positive Rate:** When the actual value is negative, how often is the prediction incorrect?

  * **Precision:** When a positive value is predicted, how often is the prediction correct?

* **Adjusting the classification threshold**

* **ROC Curves and Area Under the Curve (AUC)**

  * AUC is the percentage of the ROC plot that is underneath the curve.