hw3 110590049

tags data

2023 Educational Data Mining and Applications HW3.pdf

8.11

$$\begin{array}{rl}& \\ \text{ F-measure}=& \frac{2}{\frac{1}{precision}+\frac{1}{recall}}\\ & =\frac{2}{\frac{TP+FP}{TP}+\frac{TP+FN}{TP}}\\ & =\frac{2}{\frac{2TP+FP+FN}{TP}}\\ & =\frac{2TP}{2TP+FP+TN}\end{array}$$

8.12

$$\begin{gathered} TPR=\frac{TP}{TP+FN} \\ FPR=\frac{FP}{TN+FP} \end{gathered}$$

ROC

8.16

change the traing dataset to balance by oversampleing the fraudulent cases or undersampling nonfraudulent cases.

by threshold-moving to reduce the error chance on majority case

9.4

9.5

def distance(a,b):
    return sum([abs(a[i]-b[i]) for i in range(len(a))])
    
def KNN(input_data,k,dataset,answer):
    distances=[]
    i=0
    for data in dataset :
        distances.append({
            "distance":distance(input_data,data),
            "answer":answer[i]
        })
        i+=1
    nesrest=sorted(distances,key=lambda x:x["distance"])[:k]
    counter={key:0 for key in answer}
    for x in nesrest:
        counter[x["answer"]]+=1
    predict={key:counter[key]/k for key in counter}
    return predict
data=[[1,2,3],[0,-1,0],[1,4,4],[1,3,4]]
answer=["a","a","b","b"]
input_data=[0,0,0]
k=3
print(KNN(input_data,k,data,answer))