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k-近鄰(KNN)核心原理剖析與數(shù)據(jù)挖掘?qū)嵺`:用Scikit-learn解決分類_回歸問題 PDF 下載
匿名網(wǎng)友發(fā)布于:2025-07-11 11:31:47
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k-近鄰(KNN)核心原理剖析與數(shù)據(jù)挖掘?qū)嵺`:用Scikit-learn解決分類_回歸問題 PDF 下載 圖1

 

 

資料內(nèi)容:

 

1. 數(shù)據(jù)準備與預(yù)處理

import numpy as np
import matplotlib.pyplot as plt2. KNN分類模型構(gòu)建與評估?
from sklearn.datasets import load_digits
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
# 加載?寫數(shù)字數(shù)據(jù)集
digits = load_digits()
X, y = digits.data, digits.target
# 數(shù)據(jù)可視化
plt.figure(figsize=(10, 8))
for i in range(25):
 plt.subplot(5, 5, i+1)
 plt.imshow(digits.images[i], cmap='binary')
 plt.title(f"Label: {y[i]}")
 plt.axis('off')
plt.tight_layout()
# 數(shù)據(jù)集劃分
X_train, X_test, y_train, y_test = train_test_split(
 X, y, test_size=0.25, random_state=42
)
# 數(shù)據(jù)標準化
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)