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)
