🧠 机器学习代码示例 📈

Python 实现线性回归

from sklearn.linear_model import LinearRegression
import numpy as np

# 示例数据
X = np.array([[1], [2], [3], [4], [5]])
y = np.array([2, 4, 6, 8, 10])

model = LinearRegression().fit(X, y)
print("系数:", model.coef_)
print("截距:", model.intercept_)

使用 Scikit-learn 的分类示例

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier

data = load_iris()
X_train, X_test, y_train, y_test = train_test_split(data.data, data.target, test_size=0.2)

clf = RandomForestClassifier(n_estimators=100)
clf.fit(X_train, y_train)
print("准确率:", clf.score(X_test, y_test))

TensorFlow 神经网络示例

import tensorflow as tf

# 构建模型
model = tf.keras.Sequential([
    tf.keras.layers.Dense(10, activation='relu', input_shape=(1,)),
    tf.keras.layers.Dense(1)
])

model.compile(optimizer='adam', loss='mse')
# 训练数据
model.fit(X, y, epochs=100)

扩展阅读

如需深入了解机器学习算法实现，可访问机器学习教程获取更多示例代码。