如何使用 Python 的 Scikitlearn 库进行机器学习算法评估?

步骤：

1. 导入必要的库

import sklearn.model_selection as train_test_split

2. 加载数据

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

3. 创建评估指标

from sklearn.metrics import accuracy_score
accuracy = accuracy_score(y_test, y_pred)

4. 训练模型

from sklearn.linear_model import LinearRegression
model = LinearRegression()
model.fit(X_train, y_train)

5. 评估模型

y_pred = model.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print(f"Accuracy: {accuracy}")

示例代码：

import sklearn.model_selection as train_test_split
import numpy as np

# 加载数据
X_train = np.load('train_data.npy')
y_train = np.load('train_labels.npy')

# 创建评估指标
accuracy = train_test_split.accuracy_score

# 训练模型
model = LinearRegression()
model.fit(X_train, y_train)

# 评估模型
y_pred = model.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)

# 打印结果
print(f"Accuracy: {accuracy}")

注意：

• train_size 参数控制测试集的大小。
• accuracy_score 函数返回模型在测试集上的准确率。
• 可以使用其他评估指标，例如 precision、recall 和 F1-score。