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Statistics for AIML - Regression Metrics - PR Curve (Precision-Recall Curve) Tutorial

It is the curve between precision and recall for various threshold values. In the figure below we have 6 predictors showing their respective precision-recall curve for various threshold values. The top right part of the graph is the ideal space where we get high precision and recall. Based on our application we can choose the predictor and the threshold value. PR AUC is just the area under the curve. The higher its numerical value the better.

 

Statistics for AIML

Statistics for AIML

  • Introduction
  • Data Visualization
    • Overview
  • Descriptive statistics
    • Overview
    • Calculate Z Score
    • Covariance and Covariance matrix
    • Covariance vs. Correlation
    • QQ-Plot
    • Central Limit Theorem
  • Inferential Statistics
    • Overview
    • Hypothesis Testing
    • Statistical Test and there types
    • Bias Variance Trade Off
  • Regression Metrics
    • Overview
    • Accuracy
    • PR Curve (Precision-Recall Curve)
    • AUC-ROC Curve
    • Different types of Sampling
    • Skewness
    • Kurtosis
    • Degree Of freedom
    • Different Types Of Probability Distribution
    • Outlier
    • Bayes Theorem
    • Probability

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