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涓嬮潰鏄竴涓ず渚嬩唬鐮侊紝婕旂ず浜嗗浣曚娇鐢ㄥ鍏冮潪绾挎€у洖褰掓ā鍨嬫嫙鍚堜竴涓簩娆″嚱鏁扮殑鏁版嵁锛?/p>

import numpy as np
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import PolynomialFeatures

# 鐢熸垚鏍锋湰鏁版嵁
X = np.array([1, 2, 3, 4, 5]).reshape((-1, 1))
y = np.array([3, 6, 9, 16, 25])

# 鍒涘缓澶氶」寮忕壒寰佽浆鎹㈠櫒
poly = PolynomialFeatures(degree=2)
X_poly = poly.fit_transform(X)

# 鍒涘缓绾挎€у洖褰掓ā鍨?/span>
model = LinearRegression()

# 鎷熷悎鏁版嵁
model.fit(X_poly, y)

# 棰勬祴缁撴灉
X_test = np.array([6]).reshape((-1, 1))
X_test_poly = poly.transform(X_test)
y_pred = model.predict(X_test_poly)

print("棰勬祴缁撴灉锛?quot;, y_pred)

鍦ㄤ笂杩颁唬鐮佷腑锛岄鍏堜娇鐢≒olynomialFeatures绫诲皢杈撳叆鐗瑰緛X杞崲涓哄椤瑰紡鐗瑰緛X_poly銆傜劧鍚庯紝浣跨敤LinearRegression绫诲垱寤虹嚎鎬у洖褰掓ā鍨嬶紝骞朵娇鐢ㄦ嫙鍚堟柟娉昮it鏉ユ嫙鍚堟暟鎹€傛渶鍚庯紝浣跨敤transform鏂规硶灏嗘祴璇曟暟鎹甔_test杞崲涓哄椤瑰紡鐗瑰緛X_test_poly锛屽苟浣跨敤predict鏂规硶棰勬祴缁撴灉銆?/p>

璇锋牴鎹嚜宸辩殑鏁版嵁璋冩暣澶氶」寮忕壒寰佺殑闃舵暟(degree)锛屼互鍙婂叾浠栬秴鍙傛暟锛屼互鑾峰緱鏈€浣崇殑鎷熷悎鏁堟灉銆?/p>