Data Science Cheat Sheet 편집하기 (부분)

== Curves & Chart ==
'''[[Lift Curve]]'''

* '''X-axis''': Percent of data (typically population percentile or cumulative population)
* '''Y-axis''': Lift (ratio of model's performance vs. baseline)
* '''Application''': Helps in evaluating the effectiveness of a model in prioritizing high-response cases, often used in marketing to identify segments likely to respond to promotions.

'''[[Gain Chart]]'''

* '''X-axis''': Percent of data (typically cumulative population)
* '''Y-axis''': Cumulative gain (proportion of positives captured)
* '''Application''': Illustrates the cumulative capture of positive responses at different cutoffs, useful in customer targeting to assess the efficiency of resource allocation.

'''[[Cumulative Response Curve]]'''

* '''X-axis''': Percent of data (cumulative population)
* '''Y-axis''': Cumulative response (actual positives captured as cumulative total)
* '''Application''': Evaluates model performance by showing how many true positives are captured as more of the population is included, applicable in direct marketing to optimize campaign reach.

'''[[ROC Curve]]'''

* '''X-axis''': False Positive Rate (FPR)
* '''Y-axis''': True Positive Rate (TPR or Sensitivity)
* '''Application''': Used to evaluate the trade-off between true positive and false positive rates at various thresholds, crucial in medical testing to balance sensitivity and specificity.

'''[[Precision-Recall Curve]]'''

* '''X-axis''': Recall (True Positive Rate)
* '''Y-axis''': Precision (Positive Predictive Value)
* '''Application''': Focuses on the balance between recall and precision, especially useful in cases of class imbalance, like fraud detection or medical diagnosis, where positive class accuracy is vital.