我正在關注 senddex 的 youtube 頻道 ML 教程。
因此，當我撰寫有關如何構建自己的 KNN 演算法的代碼時，我注意到我的準確率非常低，幾乎每次都在 60 年代。我做了一些更改，但后來我逐行使用了他的代碼和相同的資料集，但不知何故，他的準確率在 95-98% 的范圍內，而我的則是 60-70%。我真的無法弄清楚如此巨大差異背后的原因。
我還有第二個問題與預測的可信度有關。置信度的值應該在 0-1 之間，對嗎？但對我來說，它們都是相同的，而且都是 70 年代。讓我用截圖來解釋
我的代碼：
# Importing libraries
import numpy as np
import pandas as pd
from collections import Counter
import warnings
import random
# Algorithm
def k_nearest(data,predict,k=5):
if len(data)>=k:
warnings.warn("stupid, your data has more dimensions than prescribed")
distances = []
for group in data: # The groups of 2s and 4s
for features in data[group]: # values in 2 and 4 respectively
#euclidean_distance = np.sqrt(np.sum((np.array(features) - np.sum(predict)) **2))
euclidean_distance = np.linalg.norm(np.array(features) - np.array(predict))
distances.append([euclidean_distance,group])
votes = [i[1] for i in sorted(distances)] # adding the sorted(ascending) group names
votes_result = Counter(votes).most_common(1)[0][0] # the most common element
confidence = float((Counter(votes).most_common(1)[0][1]))/float(k)#ocuurences of the most common element
return votes_result,confidence
#reading the data
df = pd.read_csv("breast_cancer.txt")
df.replace("?",-99999,inplace=True)
#df.replace("?", np.nan,inplace=True)
#df.dropna(inplace=True)
df.drop("id",axis = 1,inplace=True)
full_data = df.astype(float).values.tolist() # Converting to list because our function is written like that
random.shuffle(full_data)
#print(full_data[:10])
test_size = 0.2
train_set = {2:[],4:[]}
test_set = {2:[],4:[]}
train_data = full_data[:-int(test_size*len(full_data))] # Upto the last 20% of the og dateset
test_data = full_data[-int(test_size*len(full_data)):] # The last 20% of the dataset
# Populating the dictionary
for i in train_data:
train_set[i[-1]].append(i[:-1]) # appending with features and leaving out the label
for i in test_data:
test_set[i[-1]].append(i[:-1]) # appending with features and leaving out the label
# Testing
correct,total = 0,0
for group in test_set:
for data in test_set[group]:
vote,confidence = k_nearest(train_set, data,k=5)
if vote == group:
correct =1
else:
print(confidence)
total = 1
print("Accuracy is",correct/total)
鏈接到資料集Breast-cancer-wisconsin.data
uj5u.com熱心網友回復：
你的k_nearest函式有一個錯誤，你只需要回傳前 k 個距離，而不是整個串列。所以應該是：
votes = [i[1] for i in sorted(distances)[:k]]
而不是在您的代碼中：
votes = [i[1] for i in sorted(distances)]
我們可以重寫您的函式：
def k_nearest(data,predict,k=5):
distances = []
for group in data:
for features in data[group]:
euclidean_distance = np.linalg.norm(np.array(features) - np.array(predict))
distances.append([euclidean_distance,group])
votes = [i[1] for i in sorted(distances)[:k]]
votes_result = Counter(votes).most_common(1)[0][0]
confidence = float((Counter(votes).most_common(1)[0][1]))/float(k)
return votes_result,confidence
并運行您的代碼，我不太確定要替換“？” 用 -999 所以我把它讀為 na ：
import pandas as pd
from collections import Counter
import random
import numpy as np
url = 'https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/breast-cancer-wisconsin.data'
df = pd.read_csv(url,header=None,na_values="?")
df = df.dropna()
full_data = df.iloc[:,1:].astype(float).values.tolist()
random.seed(999)
random.shuffle(full_data)
test_size = 0.2
train_set = {2:[],4:[]}
test_set = {2:[],4:[]}
train_data = full_data[:-int(test_size*len(full_data))]
test_data = full_data[-int(test_size*len(full_data)):]
for i in train_data:
train_set[i[-1]].append(i[:-1])
for i in test_data:
test_set[i[-1]].append(i[:-1])
correct,total = 0,0
for group in test_set:
for data in test_set[group]:
vote,confidence = k_nearest(train_set, data,k=5)
if vote == group:
correct =1
else:
print(confidence)
total = 1
print("Accuracy is",correct/total)
給出：
1.0
0.8
1.0
0.6
0.6
0.6
0.6
Accuracy is 0.9485294117647058
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