Editing Data Science Cheat Sheet

== Confusion Matrix and F1 Score ==
'''[[Confusion Matrix]]'''
{| class="wikitable"
|-
! !!Predicted Positive!!Predicted Negative
|-
|'''Actual Positive'''||True Positive (TP)||False Negative (FN)
|-
|'''Actual Negative'''||False Positive (FP)||True Negative (TN)
|}
'''[[F1 Score]]''' = 2 * (Precision * Recall) / (Precision + Recall)

* 2 * (Positive Predictive Value * True Positive Rate) / (Positive Predictive Value + True Positive Rate)
* 2 * (TP) / (TP + FP + FN)

== Key Evaluation Metrics ==
'''[[Recall (Data Science)|True Positive Rate (TPR), Sensitivity, Recall]]'''
*TPR = Sensitivity = Recall = TP / (TP + FN)
'''[[Precision (Data Science)|Precision (Positive Predictive Value)]]'''
*Precision = TP / (TP + FP)
'''[[Specificity (Data Science)|Specificity (True Negative Rate, TNR)]]'''
*Specificity = TNR = TN / (TN + FP)
'''[[False Positive Rate|False Positive Rate (FPR)]]'''
*FPR = FP / (FP + TN)
'''Negative Predictive Value (NPV)'''
*NPV = TN / (TN + FN)
'''[[Accuracy (Data Science)|Accuracy]]'''
*Accuracy = (TP + TN) / (TP + TN + FP + FN)

== Relationships between Key Concepts ==
'''TPR (Recall) and Precision''':
*TPR represents the proportion of actual positives correctly predicted by the model, while Precision shows the proportion of predicted positives that are actually positive.
**Increasing TPR (Recall) can sometimes reduce Precision, and vice versa.
'''FPR and Specificity''':
*Specificity = (1 - FPR). In an ROC curve, FPR is plotted on the x-axis and TPR on the y-axis to visualize model performance.
'''F1 Score''':
*Defined as the harmonic mean of Precision and Recall, emphasizing their balance. F1 Score = 2 * (Precision * Recall) / (Precision + Recall)
'''Accuracy''':
*Accuracy reflects the overall model performance but may not be suitable in cases of class imbalance.