探秘机器学习算法：智慧背后的代码逻辑

import numpy as np
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt

# 创建数据
X = np.array([[1], [2], [3], [4], [5]])
y = np.array([1, 2, 1.3, 3.75, 2.25])

# 创建模型并训练
model = LinearRegression()
model.fit(X, y)

# 预测
predicted = model.predict(X)

# 可视化
plt.scatter(X, y, color='black')
plt.plot(X, predicted, color='blue', linewidth=2)
plt.xlabel("X")
plt.ylabel("y")
plt.title("Linear Regression")
plt.show()

from sklearn.linear_model import LogisticRegression
from sklearn.datasets import load_iris
import matplotlib.pyplot as plt
import numpy as np

# 加载数据集
data = load_iris()
X = data.data[:100, :2]  # 只取两个特征方便可视化
y = data.target[:100]

# 创建模型并训练
model = LogisticRegression()
model.fit(X, y)

# 可视化决策边界
x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5
y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5
xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.02),
                     np.arange(y_min, y_max, 0.02))
Z = model.predict(np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shape)

plt.contourf(xx, yy, Z, cmap=plt.cm.Paired, alpha=0.8)
plt.scatter(X[:, 0], X[:, 1], c=y, edgecolors='k', marker='o')
plt.xlabel("Feature 1")
plt.ylabel("Feature 2")
plt.title("Logistic Regression Decision Boundary")
plt.show()

from sklearn.neighbors import KNeighborsClassifier
from sklearn.datasets import load_iris
import matplotlib.pyplot as plt
import numpy as np

# 加载数据集
data = load_iris()
X = data.data[:, :2]
y = data.target

# 创建模型并训练
model = KNeighborsClassifier(n_neighbors=5)
model.fit(X, y)

# 可视化决策边界
x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.02),
                     np.arange(y_min, y_max, 0.02))
Z = model.predict(np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shape)

plt.contourf(xx, yy, Z, alpha=0.8)
plt.scatter(X[:, 0], X[:, 1], c=y, edgecolor='k')
plt.xlabel("Feature 1")
plt.ylabel("Feature 2")
plt.title("K-Nearest Neighbors Decision Boundary")
plt.show()

from sklearn.svm import SVC
from sklearn.datasets import load_iris
import numpy as np
import matplotlib.pyplot as plt

# 加载数据集
data = load_iris()
X = data.data[:, :2]  # 取前两个特征
y = data.target

# 创建模型并训练
model = SVC(kernel='linear')
model.fit(X, y)

# 可视化决策边界
x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.02),
                     np.arange(y_min, y_max, 0.02))
Z = model.predict(np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shape)

plt.contourf(xx, yy, Z, alpha=0.8)
plt.scatter(X[:, 0], X[:, 1], c=y, edgecolors='k')
plt.xlabel("Feature 1")
plt.ylabel("Feature 2")
plt.title("Support Vector Machine Decision Boundary")
plt.show()

from sklearn.tree import DecisionTreeClassifier
from sklearn.datasets import load_iris
import matplotlib.pyplot as plt
import numpy as np

# 加载数据集
data = load_iris()
X = data.data[:, :2]
y = data.target

# 创建模型并训练
model = DecisionTreeClassifier(max_depth=3)
model.fit(X, y)

# 可视化决策边界
x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.02),
                     np.arange(y_min, y_max, 0.02))
Z = model.predict(np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shape)

plt.contourf(xx, yy, Z, alpha=0.8)
plt.scatter(X[:, 0], X[:, 1], c=y, edgecolors='k')
plt.xlabel("Feature 1")
plt.ylabel("Feature 2")
plt.title("Decision Tree Decision Boundary")
plt.show()

from sklearn.ensemble import RandomForestClassifier
from sklearn.datasets import load_iris
import numpy as np
import matplotlib.pyplot as plt

# 加载数据集
data = load_iris()
X = data.data[:, :2]
y = data.target

# 创建模型并训练
model = RandomForestClassifier(n_estimators=100, max_depth=3, random_state=0)
model.fit(X, y)

# 可视化决策边界
x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
y_min, y_max = X[:, 1].min() - 1, X[:, 1].max

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