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 1) False Positive(Type 1 error): We predicted a positive outcome but it was false. Predicted an effet that did not occur. [Less Dangerous]
 2) False Negative(Type 2 error): We predicted that there won't be any effect but it actually did occur. Our prediction was negative. [More Dangerous]
+
+# Confusion Matrix
+![](cm.png)
+
+###### Accuracy Paradox:
+If 00 = more, 01 = 0, 01 = less, 11 = 0
+Accuracy increases, although we did not train. So accuarcy cannot be always trusted.
+
+#CAP: Cummulative Accuracy Profile.
+Greater area under the curve, the better is the model.  
+![](cap.png)
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